Computational Prediction of the Isoform Specificity of Cytochrome P450 Substrates by an Improved Bayesian Method

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Each CYP isoform is able to metabolize a large number of compounds, and if patients take more than one drugs during the treatment, it is possible that some drugs would be metabolized by the same CYP isoform, leading to potential drug-drug interactions and side effects. Therefore, it is necessary to investigate the isoform specificity of CYP substrates. In this study, we constructed a data set consisting of 10 major CYP isoforms associated with 776 substrates, and used machine learning methods to construct the predictive models based on the features of structural and physicochemical properties of substrates. We also proposed a new method called Improved Bayesian method, which is suitable for small data sets and is able to construct more stable and accurate predictive models compared with other traditional machine learning models. Based on this method, the predictive performance of our method got the accuracy of 86% for the independent test, which was significantly better to the existing models. We believe that our proposed method will facilitate the understanding of drug metabolisms and help the large-scale analysis of drug-drug interactions. Bioinformatics Computational Biology Drug Discovery, Design, & Development Cytochrome P450 Substrate-specificity Naïve Bayes Improved Bayesian Method Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cytochrome P450 (CYP450, CYP) is the most important drug-metabolizing enzymes in human beings [ 1 ] , which involves in the metabolism of more than 90% clinical drugs [ 2 , 3 ]. CYP450 is a superfamily of monooxygenases, which contains heme and can increase the water solubility and polarity of drugs to make them easy to excrete by oxidation reaction. 57 CYP isoforms have been discovered in human beings[ 4 ], and based on the similarity of amino acids, CYP450 can be divided to different families and subfamilies, in which xenobiotics are mainly metabolized by the families of CYP1, CYP2 and CYP3 [ 5 ]. The number of CYP isoforms is limited but the xenobiotics are various, so each CYP isoform is able to metabolize large number of compounds, which is quite different from the high specificity of most enzymes. In many cases, people will take more than one drugs during the treatment. Then it is possible that some drugs are metabolized by the same CYP isoform and lead to drug-drug interactions [ 6 , 7 ], in which the safety and efficacy of a drug can be altered by the co-administration of another drug, and this may result in serious side effects [ 6 ] and even death of the patients [ 8 , 9 ]. Therefore, it is quite important to know which CYP isoform will metabolize a drug candidate in the drug discovery stage [ 10 ]. In the past decades, both experimental and computational approaches have been developed to identify the isoform specificity of CYP450 substrate. Experimental assay is accurate but the process such as the bioluminescent and fluorescent assays [ 11 ] are quite resource-consuming. A variety of computational models including structure-based and feature-based methods have also been developed, in which the structure-based methods such as molecular dynamics simulation [ 12-14 ], quantum mechanical methods [ 15 , 16 ] and pharmacophore modeling [ 17 , 18 ] are based on the crystal structures of CYPs. However, some CYP structures are still unavailable, and because of the heme in the active site, the force field is difficult to construct, which makes these methods quite complex and time-consuming. The feature-based methods employ various physiochemical and structural features of substrates to predict which CYP isoforms will participate in the metabolism reactions based on machine learning technologies, which have been widely used in bioinformatics [ 19-21 ] and drug discovery [ 22-24 ]. These methods only employ the structures of substrates and are easy to develop and use, which are suitable for the analysis of drug metabolism [ 25 ] and drug-drug interactions [ 26 ] in large scale. A large number of predictive models have been developed to predict the isoform specificity of CYP450 substrate using various machine learning techniques such as logistic regression, decision tree, naïve Bayes, k-nearest neighbors (KNN), support vector machine (SVM) and artificial neutral network (ANN) [ 27-32 ]. Block and Henry [ 27 ] constructed independent classifiers using the descriptors including molecular connectivity, E-state indices, Spartan descriptors and structural keys, but these classifier cannot get the isoform specificity straight and the accuracy is just about 70-80%. Michielan et al. [ 28 ] developed several single-label and multi-label classification models with the accuracy of 75-95%, but the results seemed to come from the large number of negative samples because the sensitivity and precision was usually low and the more positive samples were used, the worse predictive performance was gotten. Zhang et al. [ 32 ] used decision tree, BP neutral network and ML-kNN to construct classification models, and if a substrate was metabolized by only one CYP isoform, the predictive performance is more than 90%, but if a substrate could be metabolized by more CYP isoforms, the performance would be down obviously. And above of all, these studies used small number of samples to construct the models, which limited the application fields and cause difficulties when the approaches were scaled to larger data set. In this paper, we developed a new method called Improved Bayesian method, which not only used the samples whose biological activities are known, but also the samples whose activities are unknown, and two-steps training was used in the model construction. This method may be quite suitable for small data set, and is able to construct more stable and accurate predictive models to small data set compared with other machine learning models. Based on the Improved Bayesian method, we used the metabolism information of 776 compounds and up to 10 CYP isoforms (CYP 1A1, 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, 3A4) from Meta-CYP database to construct the predictive models of isoform specificity of CYP450 substrates. Compared with naïve Bayesian method, k-nearest neighbors and support vector machine, we found the predictive performance of our method was obviously better than others and got the accuracy of 86% for the independent test, which was a significant improvement to the existing models. Results Feature selection In this paper, a total of 176 descriptors representing various structural and physicochemical properties of substrates were subject to the feature selection. As we constructed 10 separate models for different CYP isoforms, the selected descriptors were different for each model. As shown in Figure 1, CYP2A6 and CYP2E1 prefer to metabolize small molecules and CYP3A4 prefers to metabolize large molecules, but it has little correlation between molecular weight and whether a molecule can be metabolized by CYP2D6, so in the model of CYP2A6, 2E1 and 3A4, the descriptor molecular weight was selected, but in the model of CYP2D6, this descriptor was not selected. Some descriptors were selected by most models. As shown in Table 1, the descriptors about molecular size and shape seem to appear frequently, such as chi0 , VDistMa , weinerPol , Kier1 , zagreb and Std_dim3 , which implies molecular size and shape may have significant influence over the isoform specificity of CYP substrates. Other features such as hydrogen bond or molecular surface area seem also important, but the descriptor logP (Log of the octanol/water partition coefficient) did not be selected by any model, which is quite different from the studies on the other biological activities of CYP [ 18 , 33-38 ]. Maybe the mechanism of isoform specificity of CYP substrates is somewhat different from other biological activities. Model comparison Four methods including Improved Bayesian method, naïve Bayesian method, k-nearest neighbors (kNN) and support vector machine (SVM) were used to construct our classification models based on the validation set. The selected descriptors were used as input and whether a molecule can be metabolize by the CYP isoform was output (represented by 1 or 0). Leave-one-out cross-validation was used to evaluate the predictive performance, and the result is represented by AUC of ROC curve, which is shown in Table 2, Figure 2 and Figure 3. We can see Improved Bayesian method is superior to other methods in all models, while naïve Bayesian method seems to make the worst predictive performance. The results support that our method is a significant improvement to naïve Bayesian method and it is even better than some other machine learning methods such as SVM and kNN. The predictive performance also seems different among the models of different CYP isoforms. The accuracy of CYP2C8, 2C9 and 2C19 is lower than other models while CYP1A1, 2D6 and 2E1 make the best performance. This is possibly due to the different selected descriptors in each model. Independent test Besides the leave-one-out cross validation test, we also conducted an independent test on the models constructed by the improved Bayesian method, which simulates a true prediction since the model trained on one dataset is tested on an unseen dataset. The 600 molecules in the validation set were used to train the models and the other 176 molecules were used to test the models. As shown in Table 3, the predictive performance is similar to those of the leave-one-out cross validation test and the accuracy is up to 86% on average, suggesting that our method performed well on the independent test set. Discussion As the major drug-metabolizing enzymes in human beings, cytochrome P450s have been considered as the important factor of drug-drug interactions [ 6 ]. In many cases, people will take multiple drugs during the treatment, and if two drugs are metabolized by the same CYP isoform, mutual inhibition may occur and the blood concentration will abnormally increase when the patient still follow the scheduled treatment doses, which may lead to serious side effect [ 6 ]. In China, tradition Chinese medicines (TCM) are widely used, and sometimes, doctors will prescribe TCM combined with other medicines to improve the curative effect [ 39 ], but the problem of drug-drug interactions is usually neglected. The absence of rapid analysis tools is obviously a significant reason, and the computational methods with high accuracy are certainly a good solution. In this work, we constructed novel predictive models to predict the isoform specificity of CYP substrates based on Improved Bayesian method. This method not only used the samples whose biological activities are known, but also used the samples whose activities are unknown (background samples). Compared with naïve Bayesian method, the relationship of features is taken into account and we no longer need to assume the independence of conditional probability. Compared with most machine learning methods, as the relationship of features is determined by the background samples, only small amount of model variables will be changed during the training, which may lead to more stable and reliable predictive performance and less probability of overfitting. In biostatistics and bioinformatics, we often face the dataset with small sample size but large number of features, which is usually hard to analyze, but with the help of background samples, we may construct much better models. Of course, there is still some limitation in our method. We have to assume that the correlation of all features is independent of the biological activity. If this assumption is not tenable, our method is inappropriate. The common CYP isoform of two drugs is certainly a cause of drug-drug interactions, but there are still many other causes [ 40 ]. For example, some drugs can inhibit or induce the activity of CYP isoform straight, and these drugs are called inhibitors or inducers. Some drugs can interact with the transcription factors and influence the expression of CYP isoforms. All these phenomena increase the difficulty in accurate prediction of drug-drug interactions caused by CYPs, which need further investigation in future. Methods Improved Bayesian method In this paper, we will introduce a new method to construct our classification models, which is called Improved Bayesian method. In bioinformatics, due to the limitation of experimental technology, we often face the dataset with small sample size. For example, if we had 1000 drug candidates, maybe only 100 candidates were known the corresponding CYP isoforms that metabolize them, and the activities of the other 900 candidates were unknown. In most machine learning methods, we will only use the 100 compounds as samples to construct the model, but in our method, we will use the other 900 samples as well. In other words, we will not only used the samples whose biological activities are known (activity samples), but also the samples whose biological activities are unknown (background samples). As shown in Figure 4, the model will be trained by two steps. First, the model is trained by the background samples, and we can get the distribution of each feature and correlations of all features. Next, after training with the activity samples, we can get the relationship between the target and all features. Thus, the activity samples will just change small amount of variables in the model, which may lead to more stable and reliable predictive performance and less probability of over-fitting. We do not have to construct simple model for small data set, and more accurate model may be built. We will introduce the procedures of our Improved Bayesian method in detail. (a) Make all features follow normal distribution similarly, and calculate the correlation coefficient matrix R based on the normalized features Denote F ( x ) is the cumulative distribution function of feature X . Φ ( x ) and Φ -1 ( x ) are the distribution function and inverse distribution function of standard normal distribution respectively. Let (1) Then (2) Thus, X̂ follows standard normal distribution. Note that F ( x ) is calculated based on the background samples. Next, we need to calculate the correlation coefficient matrix R based on the normalized features. Of course, R is also calculated based on the background samples. Take note that the correlation coefficient may be meaningless if the variables do not follow normal distribution, so the normalization procedure is essential. (b) Estimate the joint distribution function of all features Here, we need two basic assumptions in our Improved Bayesian method. First, the correlation coefficient of two features is not dependent on the biological activity. For example, if a molecule has more atoms, its molecular weight is usually larger, and the positive correlation between the atomic number and molecular weight is irrelevant to the CYP specificity of these molecules. Second, if random variables X 1 , X 2 , … , X n follow normal distribution, n dimensional random variable ( X 1 , X 2 , … , X n ) follows n dimensional normal distribution approximately. Obviously, the two assumptions are reasonable and easy to satisfy. Based on the assumptions, the joint distribution function of ( X 1 , X 2 , … , X n ) is (3) (c) Construct the classification models based on the activity samples. Denote P ( A ) is the probability of event A . Each sample is described by n features X 1 , X 2 , … , X n , and belongs to one of the two classes C 0 or C 1 . Then, for a sample x = ( x 1 , x 2 , … , x n ) , we can calculate the probability that it belongs to C k ( k = 0 or k = 1) based on Bayesian formula: (4) In naïve Bayesian classification, we should assume the independence of conditional probability, and then (5) But in our method, based on equation (1), (3), (4), we can calculate the probability straight. (6) where F i ( x ) and f i ( x ) are the distribution function and density function of X i respectively. Φ ( x ) and φ ( x ) are the distribution function and density function of standard normal distribution respectively. Φ n ( x ) and φ n ( x ) are the joint distribution function and joint density function of n dimensional normal distribution respectively. Take note that φ n ( x ) can be expressed as (7) where Σ = diag( σ 1 , … , σ n ) is standard deviation matrix, μ = ( μ 1 , … , μ n ) is mean vector, R is correlation coefficient matrix and x = ( x 1 , x 2 , … , x n ) . As R is real symmetric matrix, it must exist orthogonal matrix P = ( α 1 , α 2 , … , α n ) , which makes P T RP = Λ = diag( λ 1 , λ 2 , …, λ n ) (8) and R -1 = PΛ -1 P T (9) where λ i and α i are the eigenvalues and corresponding eigenvectors of R . Let (10) (11) where μ i and σ i are the mean and the standard deviation of feature i in all samples respectively. μ ki and σ ki are the mean and the standard deviation of feature i in the samples belonging to class Ck . Plug equation (7)-(11) into equation (6), and take logarithm, we can get (12) Then, the larger Hk is, the more probability that x = ( x 1 , x 2 , … , x n ) belongs to C k . We can compare H 0 and H 1 to determine whether x belongs to C 0 or C 1 , or we can plot receiver operating characteristic curve (ROC curve) and select the best threshold to determine the classification. Dataset We collected 776 compounds from the Meta-CYP database, and each compound can be metabolized by at least one of the ten CYP isoforms: CYP1A1, 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1 and 3A4. The number of compounds metabolized by each CYP isoform and their distribution are shown in Table 4. We can see the total number is up to 1646, which means each compound will be metabolized by about two CYP isoforms on average. The set of 776 compounds were randomly divided into two data sets. The first set was composed of 600 samples, taken as the validation set for feature selection and model construction. The rest 176 samples constituted the independent test set for the model test. Meta-CYP database and the analysis tools based on our proposed methods are currently under construction by our laboratory, in which the information used in this paper was mainly form SuperCYP ( http://bioinformatics.charite.de/supercyp/index.php ) [41] , CYP450 Engineering Database ( https://cyped.biocatnet.de ) 42[, 43 ] and PubChem Compound ( https://www.ncbi.nlm.nih.gov/pccompound ) [44] . The detailed information about this data set is available in Supplementary Material. In this paper, background samples were gotten from DrugBank database ( http://www.drugbank.ca ) 45[, 46 ] , including the approved, experimental and investigational drugs, and 7114 compounds in all. It should be noted that inorganic compounds and polypeptides are not including in our dataset, because the features describing these compounds are quite different from other small molecules. So the models constructed in this study are not suitable for the two types of compounds. Molecular descriptors calculation We employed the molecular descriptors representing various structural and physicochemical properties of the compounds including atomic composition, molecular size, shape, weight, hydrogen bond, hydrophobicity, acidity or basicity, charge distribution and energy of chemical bonds to construct our models. To accurately calculate these molecular descriptors, the chemical structures of all compounds were first drawn by SAMM, an in-house molecular mechanics and drug design package. Next, all structures were further energy minimized by the MMFF94 force field[47] . At last, the molecular descriptors were calculated based on the energy-minimized structures by SAMM. After removing the descriptors that were highly correlated or had the same value in almost all compounds, 176 molecular descriptors including 134 two-dimensional and 42 three-dimensional descriptors were gotten and would be subject to further feature selection. Feature selection Feature selection is performed prior to the construction of the predictive models in order to eliminate the redundant features and identify the most relevant features [ 48-50 ] . Minimum Redundancy Maximum Relevance (MRMR)[51] was applied to select the best features in this paper, which is a powerful method for getting useful information from complex systems through maximizing the mutual information between the selected features and the classification target, and in the meanwhile, minimizing the correlation of the features themselves[52] . In this work, we selected the top 20 features with the highest scores in the model construction. Model comparison and evaluation Besides Improved Bayesian method, we also used naïve Bayesian method, k-nearest neighbors and support vector machine[53] to construct our classification models based on the validation set for model comparison. In naïve Bayesian method, the Laplace smoothing factor was 1. The parameters of k-nearest neighbors was k = 5 , Euclidean distance and distance weighting function = 1/ d . The kernel function in support vector machine was radial basis function and other parameters were optimized by LIBSVM[54] . For the constructed models, we would use AUC (area under the curve) of receiver operating characteristic curve (ROC) for model evaluation. Besides that, we would calculate specificity, sensitivity, accuracy as well. where TP, FP, TN, FN means true positive, false positive, true negative and false negative respectively. Conclusions In this study, we employed machine learning methods to predict the CYP isoform specificity of 776 compounds with their corresponding CYP enzymes, including CYP 1A1, 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1 and 3A4. The improved Bayesian method was proposed and applied in the model construction based on the features of structural and physicochemical properties of substrates. In comparison with the naïve Bayesian method, k-nearest neighbors and support vector machine, it was found that the predictive models built by our method achieved the best accuracy, which is up to 86% accuracy on average on the independent test. Using such a model, we can efficiently and reliably estimate the metabolic paths of a given compound involved in CYP-mediated metabolism of these ten CYPs. We believed that our proposed method will be potentially useful in large-scale in silico drug screening and drug-drug interaction analysis. Acknowledgements This work was supported by the fundings from National Key Research and Development Program of China (Contract No. 2016YFA0501703), and National Natural Science Foundation of China (Grant No. 31601074, 61872094, and 61832019). References Guengerich FP: Cytochrome P450s and other enzymes in drug metabolism and toxicity . AAPS J 2006, 8 (1):E101-111. 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Eur J Med Chem 1997, 4 :307-312. Balaban AT: Five New Topological Indices for the Branching of Tree-Like Graphs . Theor Chim Acta 1979, 53 :355-375. Gasteiger J, Marsili M: Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges . Tetrahedron 1980, 36 (22):3219-3228. Tables Table 1. The features selected by most models and their descriptions Name Description a_heavy Number of heavy atoms a_acc Number of hydrogen bond acceptor atoms a_IC Atom information content (total). Let n i be the number of occurrences of atomic number i in the molecule, p i = n i / n where n is the sum of the n i . Then a_IC = - n ∑ p i log p i b_single Number of single bonds (not including aromatic bonds). bpol Sum of the absolute value of the difference between atomic polarizabilities [ 55 ] of all bonded atoms in the molecule chi0 Atomic connectivity index (order 0) [ 56 , 57 ] . The sum of 1/sqrt( d i ) over all heavy atoms i with d i > 0, where d i is the number of bonded neighbors of the atom i in the hydrogen suppressed graph VDistMa Sum of log 2 m - D ij log 2 D ij / m over all i and j , where D ij is the length of the shortest path from atoms i to j , and m is the sum of D ij weinerPol Wiener polarity number: half the sum of all the distance matrix entries with a value of 3 as defined in [ 58 ] Kier1 First kappa shape index: ( n -1) 2 / m 2 , where n and m denotes the number of atoms and the number of bonds in the hydrogen suppressed graph respectively [ 56 ] PEOE_VSA_PPOS Total positive polar van der Waals surface area. Sum of the v i such that q i is greater than 0.2. The v i is calculated using a connection table approximation and q i is the partial charge of atom i calculated by the Partial Equalization of Orbital Electronegativities (PEOE) method [ 59 ] vdw_area Area of van der Waals surface mr Molecular refractivity zagreb Zagreb index: the sum of d i 2 over all heavy atoms i Std_dim3 Standard dimension 3: the square root of the third largest eigenvalue of the covariance matrix of the atomic coordinates. A standard dimension is equivalent to the standard deviation along a principal component axis Table 2. The validation result of ten CYP predictive models by four methods, represented by AUC. 1A1 1A2 2A6 2B6 2C19 2C8 2C9 2D6 2E1 3A4 Improved Bayesian method 0.92 0.84 0.87 0.88 0.84 0.80 0.83 0.92 0.91 0.88 naïve Bayesian method 0.79 0.70 0.81 0.76 0.72 0.69 0.71 0.82 0.85 0.82 kNN 0.82 0.75 0.84 0.80 0.75 0.73 0.77 0.84 0.88 0.81 SVM 0.85 0.73 0.83 0.80 0.75 0.76 0.79 0.88 0.89 0.82 Table 3. The results of independent test set calculated by the improved Bayesian method 1A1 1A2 2A6 2B6 2C19 2C8 2C9 2D6 2E1 3A4 Avg. Specificity 0.95 0.93 0.95 0.94 0.87 0.91 0.90 0.86 0.95 0.72 0.90 Sensitivity 0.58 0.48 0.75 0.53 0.71 0.56 0.66 0.76 0.75 0.86 0.66 Accuracy 0.90 0.81 0.93 0.86 0.85 0.86 0.85 0.83 0.93 0.78 0.86 Table 4. The number of substrates metabolized by each CYP450 isoform 1A1 1A2 2A6 2B6 2C19 2C8 2C9 2D6 2E1 3A4 Total 87 167 83 109 144 115 166 225 127 423 1646 Supplementary Files supplement1.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-961","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":88661,"identity":"b570702e-970b-4e8b-af35-1d0471e42b22","order_by":1,"name":"Hao Dai","email":"","orcid":"","institution":"Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Dai","suffix":""},{"id":88662,"identity":"459333d4-1028-4f0d-881d-236ba4becae6","order_by":2,"name":"Yu-Xi Zheng","email":"","orcid":"","institution":"State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yu-Xi","middleName":"","lastName":"Zheng","suffix":""},{"id":88663,"identity":"c77a377f-775f-439f-b04f-3afb00a18d31","order_by":3,"name":"Xiao-Qi Shan","email":"","orcid":"","institution":"State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiao-Qi","middleName":"","lastName":"Shan","suffix":""},{"id":88664,"identity":"f8d0b636-5599-4088-a206-e2b6c8fb9073","order_by":4,"name":"Yan-Yi Chu","email":"","orcid":"","institution":"State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yan-Yi","middleName":"","lastName":"Chu","suffix":""},{"id":88665,"identity":"ceb924c6-f863-405f-bbe0-5a91ca1a4891","order_by":5,"name":"Wei Wang","email":"","orcid":"","institution":"School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":88666,"identity":"c11fcb4d-aa1c-4a93-9585-0100d41b9fdc","order_by":6,"name":"Yi Xiong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACZjApIcfAcADBJUqLMQPDYWK1QEFiA0Q1EVoMjjMfe/i1zSJ9fuP5YxIMFdaJDexnD+DVItnMlm4s2yaRu+HAYTYJhjPpiQ08eQl4tfAz85hJS4K0MAC1MLYdTmyQ4DHAq4UNqiVdvgGk5R8RWkC2SH5sk0hgADmMsYEILUC/pEkznJMwBPrF2CLhWLpxG08Ofi0G5w8fk/xRVicvP+Pgwxsfaqxl+9nP4NcCAsy8bEBS4gADQwLIdwTVAwHjjz8gXzUQo3YUjIJRMApGIgAA9tE+MHMnoxMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-2910-6725","institution":"State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Xiong","suffix":""},{"id":88667,"identity":"47d19aff-6236-4af4-8c97-8521000449f1","order_by":7,"name":"Dong-Qing Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYDCCA0CcUMHADObwEK/lDAMzD2laGNugqonSwnc8x/DBw3l17PYSCYwP3rYxyJsT0iJ55o2xQeK2w8w8EgnMhnPbGAx3NhDQYnAjd5tE4rYDIC1s0rxtDAkGBwhr2f4jcU4dSAv7b2K1bGNIbGAG28JMlBbJM+8/SyQcA/rlzMNmyTnnJAw3ENLCdzwt8eOPmrpk9vbkgx/elNnIE7QFGI9gMhkYOw1AWoKgergWO2KUjoJRMApGwQgFAPJdPru682IVAAAAAElFTkSuQmCC","orcid":"","institution":"State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Dong-Qing","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2019-05-21 02:38:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.2.9738/v1","doiUrl":"https://doi.org/10.21203/rs.2.9738/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":2612559,"identity":"7f68b900-1200-4282-b3b7-e99d520bf434","added_by":"dbd29d3e-ebd5-4de2-9e49-4198bc6730d4","created_at":"2020-09-25 20:53:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":81823,"visible":true,"origin":"","legend":"The proportion of the substrates metabolized by CYP2A6，2D6，2E1 or 3A4 in each group. All substrates are divided into 5 groups based on their molecular weight: very small (\u003c 207), small (207-280), medium (280-326), large (326-405) and very large (\u003e405). The number of substrates in each group is almost the same","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-961/v1/figure_1.png"},{"id":2612562,"identity":"da192f55-bda2-4de2-85df-e0d928e5d8f0","added_by":"dbd29d3e-ebd5-4de2-9e49-4198bc6730d4","created_at":"2020-09-25 20:53:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":855658,"visible":true,"origin":"","legend":"The comparison of the validation results by four methods and ten CYP models","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-961/v1/figure_2.png"},{"id":2612571,"identity":"67f9f028-1f62-4331-932b-ddb107ad6d40","added_by":"dbd29d3e-ebd5-4de2-9e49-4198bc6730d4","created_at":"2020-09-25 20:53:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2081382,"visible":true,"origin":"","legend":"ROC curves of ten CYP models with four methods","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-961/v1/figure_3.png"},{"id":2612558,"identity":"9a4c8abb-2f55-44a0-aa9b-cf8b4facac41","added_by":"dbd29d3e-ebd5-4de2-9e49-4198bc6730d4","created_at":"2020-09-25 20:53:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":514176,"visible":true,"origin":"","legend":"Illustration of the two-steps training in Improved Bayesian method. The gray circle represents each feature and the black circle represents the target. The gray lines and the black arrows represent the relationship among features and the relationship between features and the target respectively","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-961/v1/figure_4.png"},{"id":13466866,"identity":"654b500a-3f74-4d31-acab-40be13c15a3e","added_by":"auto","created_at":"2021-09-16 20:52:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2325917,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-961/v1/907c0c1b-ee3b-4447-adc3-3e6c35aa17e3.pdf"},{"id":2612557,"identity":"fb12a0f0-3c75-4594-ba5d-ce90ce34c211","added_by":"38143e50-3cad-4f1e-a5b9-32145d95299b","created_at":"2020-09-25 20:53:00","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":702464,"visible":true,"origin":"","legend":"","description":"","filename":"supplement1.doc","url":"https://assets-eu.researchsquare.com/files/rs-961/v1/supplement_1.doc"}],"financialInterests":"","formattedTitle":"\u003cp\u003eComputational Prediction of the Isoform Specificity of Cytochrome P450 Substrates by an Improved Bayesian Method\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCytochrome P450 (CYP450, CYP) is the most important drug-metabolizing enzymes in human beings [\u003ca href=\"#_ENREF_1\"\u003e1\u003c/a\u003e] , which involves in the metabolism of more than 90% clinical drugs [\u003ca href=\"#_ENREF_2\"\u003e2\u003c/a\u003e, \u003ca href=\"#_ENREF_3\"\u003e3\u003c/a\u003e]. CYP450 is a superfamily of monooxygenases, which contains heme and can increase the water solubility and polarity of drugs to make them easy to excrete by oxidation reaction. 57 CYP isoforms have been discovered in human beings[\u003ca href=\"#_ENREF_4\"\u003e4\u003c/a\u003e], and based on the similarity of amino acids, CYP450 can be divided to different families and subfamilies, in which xenobiotics are mainly metabolized by the families of CYP1, CYP2 and CYP3 [\u003ca href=\"#_ENREF_5\"\u003e5\u003c/a\u003e]. The number of CYP isoforms is limited but the xenobiotics are various, so each CYP isoform is able to metabolize large number of compounds, which is quite different from the high specificity of most enzymes. In many cases, people will take more than one drugs during the treatment. Then it is possible that some drugs are metabolized by the same CYP isoform and lead to drug-drug interactions [\u003ca href=\"#_ENREF_6\"\u003e6\u003c/a\u003e, \u003ca href=\"#_ENREF_7\"\u003e7\u003c/a\u003e], in which the safety and efficacy of a drug can be altered by the co-administration of another drug, and this may result in serious side effects [\u003ca href=\"#_ENREF_6\"\u003e6\u003c/a\u003e] and even death of the patients [\u003ca href=\"#_ENREF_8\"\u003e8\u003c/a\u003e, \u003ca href=\"#_ENREF_9\"\u003e9\u003c/a\u003e]. Therefore, it is quite important to know which CYP isoform will metabolize a drug candidate in the drug discovery stage [\u003ca href=\"#_ENREF_10\"\u003e10\u003c/a\u003e].\u003c/p\u003e\n\u003cp\u003eIn the past decades, both experimental and computational approaches have been developed to identify the isoform specificity of CYP450 substrate. Experimental assay is accurate but the process such as the bioluminescent and fluorescent assays [\u003ca href=\"#_ENREF_11\"\u003e11\u003c/a\u003e] are quite resource-consuming. A variety of computational models including structure-based and feature-based methods have also been developed, in which the structure-based methods such as molecular dynamics simulation [\u003ca href=\"#_ENREF_12\"\u003e12-14\u003c/a\u003e], quantum mechanical methods [\u003ca href=\"#_ENREF_15\"\u003e15\u003c/a\u003e, \u003ca href=\"#_ENREF_16\"\u003e16\u003c/a\u003e] and pharmacophore modeling [\u003ca href=\"#_ENREF_17\"\u003e17\u003c/a\u003e, \u003ca href=\"#_ENREF_18\"\u003e18\u003c/a\u003e] are based on the crystal structures of CYPs. However, some CYP structures are still unavailable, and because of the heme in the active site, the force field is difficult to construct, which makes these methods quite complex and time-consuming. The feature-based methods employ various physiochemical and structural features of substrates to predict which CYP isoforms will participate in the metabolism reactions based on machine learning technologies, which have been widely used in bioinformatics [\u003ca href=\"#_ENREF_19\"\u003e19-21\u003c/a\u003e] and drug discovery [\u003ca href=\"#_ENREF_22\"\u003e22-24\u003c/a\u003e]. These methods only employ the structures of substrates and are easy to develop and use, which are suitable for the analysis of drug metabolism [\u003ca href=\"#_ENREF_25\"\u003e25\u003c/a\u003e] and drug-drug interactions [\u003ca href=\"#_ENREF_26\"\u003e26\u003c/a\u003e] in large scale.\u003c/p\u003e\n\u003cp\u003eA large number of predictive models have been developed to predict the isoform specificity of CYP450 substrate using various machine learning techniques such as logistic regression, decision tree, na\u0026iuml;ve Bayes, k-nearest neighbors (KNN), support vector machine (SVM) and artificial neutral network (ANN) [\u003ca href=\"#_ENREF_27\"\u003e27-32\u003c/a\u003e]. Block and Henry [\u003ca href=\"#_ENREF_27\"\u003e27\u003c/a\u003e] constructed independent classifiers using the descriptors including molecular connectivity, E-state indices, Spartan descriptors and structural keys, but these classifier cannot get the isoform specificity straight and the accuracy is just about 70-80%. \u0026nbsp;Michielan et al. [\u003ca href=\"#_ENREF_28\"\u003e28\u003c/a\u003e] developed several single-label and multi-label classification models with the accuracy of 75-95%, but the results seemed to come from the large number of negative samples because the sensitivity and precision was usually low and the more positive samples were used, the worse predictive performance was gotten. Zhang et al. [\u003ca href=\"#_ENREF_32\"\u003e32\u003c/a\u003e] used decision tree, BP neutral network and ML-kNN to construct classification models, and if a substrate was metabolized by only one CYP isoform, the predictive performance is more than 90%, but if a substrate could be metabolized by more CYP isoforms, the performance would be down obviously. And above of all, these studies used small number of samples to construct the models, which limited the application fields and cause difficulties when the approaches were scaled to larger data set.\u003c/p\u003e\n\u003cp\u003eIn this paper, we developed a new method called Improved Bayesian method, which not only used the samples whose biological activities are known, but also the samples whose activities are unknown, and two-steps training was used in the model construction. This method may be quite suitable for small data set, and is able to construct more stable and accurate predictive models to small data set compared with other machine learning models. Based on the Improved Bayesian method, we used the metabolism information of 776 compounds and up to 10 CYP isoforms (CYP 1A1, 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, 3A4) from Meta-CYP database to construct the predictive models of isoform specificity of CYP450 substrates. Compared with na\u0026iuml;ve Bayesian method, k-nearest neighbors and support vector machine, we found the predictive performance of our method was obviously better than others and got the accuracy of 86% for the independent test, which was a significant improvement to the existing models.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eFeature selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this paper, a total of 176 descriptors representing various structural and physicochemical properties of substrates were subject to the feature selection. As we constructed 10 separate models for different CYP isoforms, the selected descriptors were different for each model. As shown in Figure 1, CYP2A6 and CYP2E1 prefer to metabolize small molecules and CYP3A4 prefers to metabolize large molecules, but it has little correlation between molecular weight and whether a molecule can be metabolized by CYP2D6, so in the model of CYP2A6, 2E1 and 3A4, the descriptor molecular weight was selected, but in the model of CYP2D6, this descriptor was not selected.\u003c/p\u003e\n\u003cp\u003eSome descriptors were selected by most models. As shown in Table 1, the descriptors about molecular size and shape seem to appear frequently, such as \u003cem\u003echi0\u003c/em\u003e, \u003cem\u003eVDistMa\u003c/em\u003e, \u003cem\u003eweinerPol\u003c/em\u003e, \u003cem\u003eKier1\u003c/em\u003e, \u003cem\u003ezagreb\u003c/em\u003e and \u003cem\u003eStd_dim3\u003c/em\u003e, which implies molecular size and shape may have significant influence over the isoform specificity of CYP substrates. Other features such as hydrogen bond or molecular surface area seem also important, but the descriptor \u003cem\u003elogP\u003c/em\u003e (Log of the octanol/water partition coefficient) did not be selected by any model, which is quite different from the studies on the other biological activities of CYP [\u003ca href=\"#_ENREF_18\"\u003e18\u003c/a\u003e, \u003ca href=\"#_ENREF_33\"\u003e33-38\u003c/a\u003e]. Maybe the mechanism of isoform specificity of CYP substrates is somewhat different from other biological activities.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel comparison\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour methods including Improved Bayesian method, na\u0026iuml;ve Bayesian method, k-nearest neighbors (kNN) and support vector machine (SVM) were used to construct our classification models based on the validation set. The selected descriptors were used as input and whether a molecule can be metabolize by the CYP isoform was output (represented by 1 or 0). Leave-one-out cross-validation was used to evaluate the predictive performance, and the result is represented by AUC of ROC curve, which is shown in Table 2, Figure 2 and Figure 3.\u003c/p\u003e\n\u003cp\u003eWe can see Improved Bayesian method is superior to other methods in all models, while na\u0026iuml;ve Bayesian method seems to make the worst predictive performance. The results support that our method is a significant improvement to na\u0026iuml;ve Bayesian method and it is even better than some other machine learning methods such as SVM and kNN. The predictive performance also seems different among the models of different CYP isoforms. The accuracy of CYP2C8, 2C9 and 2C19 is lower than other models while CYP1A1, 2D6 and 2E1 make the best performance. This is possibly due to the different selected descriptors in each model.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBesides the leave-one-out cross validation test, we also conducted an independent test on the models constructed by the improved Bayesian method, which simulates a true prediction since the model trained on one dataset is tested on an unseen dataset. The 600 molecules in the validation set were used to train the models and the other 176 molecules were used to test the models. As shown in Table 3, the predictive performance is similar to those of the leave-one-out cross validation test and the accuracy is up to 86% on average, suggesting that our method performed well on the independent test set.\u003c/p\u003e"},{"header":"Discussion","content":" \u003cp\u003eAs the major drug-metabolizing enzymes in human beings, cytochrome P450s have been considered as the important factor of drug-drug interactions [\u003ca href=\"#_ENREF_6\"\u003e6\u003c/a\u003e]. In many cases, people will take multiple drugs during the treatment, and if two drugs are metabolized by the same CYP isoform, mutual inhibition may occur and the blood concentration will abnormally increase when the patient still follow the scheduled treatment doses, which may lead to serious side effect [\u003ca href=\"#_ENREF_6\"\u003e6\u003c/a\u003e]. In China, tradition Chinese medicines (TCM) are widely used, and sometimes, doctors will prescribe TCM combined with other medicines to improve the curative effect [\u003ca href=\"#_ENREF_39\"\u003e39\u003c/a\u003e], but the problem of drug-drug interactions is usually neglected. The absence of rapid analysis tools is obviously a significant reason, and the computational methods with high accuracy are certainly a good solution.\u003c/p\u003e\n\u003cp\u003eIn this work, we constructed novel predictive models to predict the isoform specificity of CYP substrates based on Improved Bayesian method. This method not only used the samples whose biological activities are known, but also used the samples whose activities are unknown (background samples). Compared with na\u0026iuml;ve Bayesian method, the relationship of features is taken into account and we no longer need to assume the independence of conditional probability. Compared with most machine learning methods, as the relationship of features is determined by the background samples, only small amount of model variables will be changed during the training, which may lead to more stable and reliable predictive performance and less probability of overfitting. In biostatistics and bioinformatics, we often face the dataset with small sample size but large number of features, which is usually hard to analyze, but with the help of background samples, we may construct much better models. Of course, there is still some limitation in our method. We have to assume that the correlation of all features is independent of the biological activity. If this assumption is not tenable, our method is inappropriate.\u003c/p\u003e\n\u003cp\u003eThe common CYP isoform of two drugs is certainly a cause of drug-drug interactions, but there are still many other causes [\u003ca href=\"#_ENREF_40\"\u003e40\u003c/a\u003e]. For example, some drugs can inhibit or induce the activity of CYP isoform straight, and these drugs are called inhibitors or inducers. Some drugs can interact with the transcription factors and influence the expression of CYP isoforms. All these phenomena increase the difficulty in accurate prediction of drug-drug interactions caused by CYPs, which need further investigation in future.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eImproved Bayesian method\u003c/b\u003e\u003c/p\u003e\n\u003cp\u003eIn this paper, we will introduce a new method to construct our classification models, which is called Improved Bayesian method. In bioinformatics, due to the limitation of experimental technology, we often face the dataset with small sample size. For example, if we had 1000 drug candidates, maybe only 100 candidates were known the corresponding CYP isoforms that metabolize them, and the activities of the other 900 candidates were unknown. In most machine learning methods, we will only use the 100 compounds as samples to construct the model, but in our method, we will use the other 900 samples as well. In other words, we will not only used the samples whose biological activities are known (activity samples), but also the samples whose biological activities are unknown (background samples). \u003c/p\u003e\n\u003cp\u003eAs shown in Figure 4, the model will be trained by two steps. First, the model is trained by the background samples, and we can get the distribution of each feature and correlations of all features. Next, after training with the activity samples, we can get the relationship between the target and all features. Thus, the activity samples will just change small amount of variables in the model, which may lead to more stable and reliable predictive performance and less probability of over-fitting. We do not have to construct simple model for small data set, and more accurate model may be built.\u003c/p\u003e\n\u003cp\u003eWe will introduce the procedures of our Improved Bayesian method in detail. \u003c/p\u003e\n\u003cp\u003e(a) Make all features follow normal distribution similarly, and calculate the correlation coefficient matrix \u003cspan style=\"font-family:serif\"\u003e\u003cb\u003e\u003ci\u003eR \u003c/i\u003e\u003c/b\u003e\u003c/span\u003ebased on the normalized features \u003c/p\u003e\n\u003cp\u003eDenote \u003cspan style=\"font-family:serif\"\u003e\u003ci\u003eF\u003c/i\u003e(\u003ci\u003ex\u003c/i\u003e)\u003c/span\u003e is the cumulative distribution function of feature \u003ci style=\"font-family:serif\"\u003eX\u003c/i\u003e. \u003cspan style=\"font-family:serif\"\u003e\u003ci\u003eΦ\u003c/i\u003e(\u003ci\u003ex\u003c/i\u003e)\u003c/span\u003e and \u003cspan style=\"font-family:serif\"\u003e\u003ci\u003eΦ\u003c/i\u003e\u003csup\u003e-1\u003c/sup\u003e(\u003ci\u003ex\u003c/i\u003e)\u003c/span\u003e are the distribution function and inverse distribution function of standard normal distribution respectively. Let\u003c/p\u003e\u003ctable\u003e\u003ctbody\u003e\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003cdiv class=\"embedded\" id=\"_1555409866\"/\u003e \u003cimg 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\"/\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e(2)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003eThus, \u003cspan style=\"font-family:serif\"\u003eX\u0026#770; \u003c/span\u003e follows standard normal distribution. Note that \u003cspan style=\"font-family:serif\"\u003e\u003ci\u003eF\u003c/i\u003e(\u003ci\u003ex\u003c/i\u003e)\u003c/span\u003e is calculated based on the background samples. \u003c/p\u003e\n\u003cp\u003eNext, we need to calculate the correlation coefficient matrix \u003cspan style=\"font-family:serif\"\u003e\u003cb\u003e\u003ci\u003eR \u003c/i\u003e\u003c/b\u003e\u003c/span\u003ebased on the normalized features. Of course, \u003cspan style=\"font-family:serif\"\u003e\u003cb\u003e\u003ci\u003eR\u003c/i\u003e\u003c/b\u003e\u003c/span\u003e is also calculated based on the background samples. Take note that the correlation coefficient may be meaningless if the variables do not follow normal distribution, so the normalization procedure is essential.\u003c/p\u003e\n\u003cp\u003e(b) Estimate the joint distribution function of all features \u003c/p\u003e\n\u003cp\u003eHere, we need two basic assumptions in our Improved Bayesian method. First, the correlation coefficient of two features is not dependent on the biological activity. For example, if a molecule has more atoms, its molecular weight is usually larger, and the positive correlation between the atomic number and molecular weight is irrelevant to the CYP specificity of these molecules. Second, if random variables \u003cspan style=\"font-family:serif\"\u003e\u003ci\u003eX\u003c/i\u003e\u003csub\u003e1\u003c/sub\u003e, \u003ci\u003eX\u003c/i\u003e\u003csub\u003e2\u003c/sub\u003e, … , \u003ci\u003eX\u003csub\u003en\u003c/sub\u003e\u003c/i\u003e\u003c/span\u003e follow normal distribution, \u003ci\u003en\u003c/i\u003e dimensional random variable \u003cspan style=\"font-family:serif\"\u003e(\u003ci\u003eX\u003c/i\u003e\u003csub\u003e1\u003c/sub\u003e, \u003ci\u003eX\u003c/i\u003e\u003csub\u003e2\u003c/sub\u003e, … , \u003ci\u003eX\u003csub\u003en\u003c/sub\u003e\u003c/i\u003e)\u003c/span\u003e follows \u003cspan style=\"font-family:serif\"\u003e\u003ci\u003en\u003c/i\u003e\u003c/span\u003e dimensional normal distribution approximately. Obviously, the two assumptions are reasonable and easy to satisfy.\u003c/p\u003e\n\u003cp\u003eBased on the assumptions, the joint distribution function of \u003cspan style=\"font-family:serif\"\u003e(\u003ci\u003eX\u003c/i\u003e\u003csub\u003e1\u003c/sub\u003e, \u003ci\u003eX\u003c/i\u003e\u003csub\u003e2\u003c/sub\u003e, … , \u003ci\u003eX\u003csub\u003en\u003c/sub\u003e\u003c/i\u003e)\u003c/span\u003e is\u003c/p\u003e\u003ctable\u003e\u003ctbody\u003e\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003cdiv class=\"embedded\" id=\"_1555410816\"/\u003e\n\u003cimg 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\" /\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e(3)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003e(c) Construct the classification models based on the activity samples. \u003c/p\u003e \u003cp\u003eDenote \u003cspan style=\"font-famaily:serif;\"\u003e\u003ci\u003eP\u003c/i\u003e(\u003ci\u003eA\u003c/i\u003e)\u003c/span\u003e is the probability of event \u003ci style=\"font-family:serif;\"\u003eA\u003c/i\u003e. Each sample is described by \u003ci style=\"font-family:serif;\"\u003en \u003c/i\u003efeatures \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eX\u003c/i\u003e\u003csub\u003e1\u003c/sub\u003e, \u003ci\u003eX\u003c/i\u003e\u003csub\u003e2\u003c/sub\u003e, … , \u003ci\u003eX\u003csub\u003en\u003c/sub\u003e\u003c/i\u003e\u003c/span\u003e, and belongs to one of the two classes \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eC\u003c/i\u003e\u003csub\u003e0\u003c/sub\u003e\u003c/span\u003e or \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eC\u003c/i\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/span\u003e. Then, for a sample \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003ex\u003c/i\u003e = (\u003ci\u003ex\u003c/i\u003e\u003csub\u003e1\u003c/sub\u003e, \u003ci\u003ex\u003c/i\u003e\u003csub\u003e2\u003c/sub\u003e, … , \u003ci\u003ex\u003csub\u003en\u003c/sub\u003e\u003c/i\u003e)\u003c/span\u003e, we can calculate the probability that it belongs to \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eC\u003csub\u003ek\u003c/sub\u003e\u003c/i\u003e (\u003ci\u003ek \u003c/i\u003e= 0 \u003c/span\u003eor\u003cspan style=\"font-family:serif;\"\u003e \u003ci\u003ek \u003c/i\u003e= 1)\u003c/span\u003e based on Bayesian formula:\u003c/p\u003e\n\u003ctable\u003e\u003ctbody\u003e\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003cdiv class=\"embedded\" id=\"_1555410155\"/\u003e\n\u003cimg 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\"/\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e(4)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003eIn naïve Bayesian classification, we should assume the independence of conditional probability, and then\u003c/p\u003e\n\u003ctable\u003e\u003ctbody\u003e\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003cdiv class=\"embedded\" id=\"_1555410287\"/\u003e\n\u003cimg 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kSah0QqmCb/S0PS9833f2mufcy/3dt/b3TSQ2Qf67H3237fX2mutvc4++5xbqVZrtUol6KiGWugKlVCrhVCp4IQ4jxr+VWqIV1KJmF5BiSoKdFnRokqpDhtACwSpVVG+vqxhqTm0lPEz/7P8Zf3L9ifb32IyyfNPnn8n2/+o1OD5wMuRewPvRM5NoHNC1wXX8lfkCFkR+UKSSYkjHCKk06dBYGc6Q0ViDQWYolwrZHCpDBIzvvEu8z/LH24usv5l+5Ptb55/8vx74fyPSrUGx0e+R3RXENf8a64Kzua0+MKOkmOqKno+Cmg1J9YP9Ke6Yl2vpNAcJq4CVeDwqI2MH3kBbmf+Z/mD2sRbhahpCOLCqqsS87P+QVmy/Ykike1vnn9ww5DnX80fY/kflRoOaU6yITGCZD6ecgNs6zaex7tSOi3mkZnmpQbSIytPVw69qcbDl4uYnqo7RsbP/M/yl/XPjEO2P9QFt43Z/ub5J8+/E/E/8KiLjg9218jJiapF/eLhvgqLcHUm6h2TebcpVeS+nUrhabpyqihOWj3yi0J1VZ/JXXKIMn7mP6Upy19Slax/2f5cIvb3xPHj4cDrr4ebb+4JV19zdfjxv/043PWpvwyzu2djTsj2P89/l9/8j0ddfJIYDy6n48I31vlSujkoKJMKegWtwGNHkMx1WrUxp4hlShXcogsg1kda+VLL+Rk/8z+uBGb5M73iucuiUXGKgPf+Wf8ic1LAmzIe2f6IDTyJNziVDW7DZSv7u379+vDN1d8Mz2z7Sfjyl78cjh4/Gvr+vi+sWbOGVbL85fkPUgBdu4z0Tys+VUw0fJsoKYcMBqYdKAkWekqHUWYTEkml5kSzi3KmBCjuDMASkdX3zJjhRXiJI+Nn/mf5y/qX7Q+MYXr8f+nY37fffjvMmcPVnUoYGTmH+Jzw3e99Nzz0lQez/c/zn6mtT/Gc0C+D+b/Y4xP7y37zmRY3HlscZyojhL6KJN15MqOcrYI8RU8GZfHfiyiVOXSRtL7EZ2ZYHeOjjfIRa2f8zP8sf1n/zDTQKGT7c1Ht7/f/ZWN4/PEnwhsDA+HG938gTJt+RTh+7Fi49tprfY6LZjxa8Gz/8/x3ic//cY9PFFg3thRjOifoPLcAdXEPjw5b6zF3hZlIpCckQbeQRirt1YiOkKWk9aDYVgwErRPwrGWDQlrGz/zP8pf1L9ufZCxpKafa/k7ruiIsf+Th8K8/+EF47mc/C5/51Kdll7L9x2Dk+e+ynP/Tio8rFF/ySqs9XKGRGwPaPBYL1rtAXjvqJ8pqeYgair/oQ1kbcG6sNFqAw+S+jreQ8TP/s/zZ1FbSOtMdKVPUqahF1DjXKMbtyPqX7Q9kaBLs76k/nQ7XXH11GDwyqM3MixYtks3+xB2fCOu+8x2JW5Y/n72y/l029oebm+ls2Cd3qCnulkBvyuOpeL2zotUgahf+07nRak9pmVMOD/OYRY54JIYMeGT8zP8sf9QEaokph85RT5hjyVn/yjdL2f7EvYEX0P7u/PnOsGDBAnx0H8KICaG3d57e8Dp18kSYcdVVtjf0AuLbxJHlP+s/ZADyN1n6n1Z86LnwlWouX9rKcrS6Smdc208lgXJoKJEUePYHfxZjyB+xqKK0PR6jU1PcwbNUXEPCa5ARiJWQkfEz/7P8Zf1zs5DtjyzrJWZ/+fo6+8V//J/tv9jACYyzGI48/10O83/96+wauGIvTiHUykgnOT4U+lgA3pM5TRp2xLHqoyN6aF38DL9+z8vy6F+pDbpBuIilY/sZXzu/wY3MfzcqUTRikOUPfMn6l+1Ptr8yEHn+yfOvFg0wP/Cxazv+h634RAXyiTY5ND7R8DYUjosclPRDpcxkDQbuuqCclos8HXnMKj3+SnWYkQCtXLqMEW+1lvHBq8x/yUOWP6hNsfoKzZF+KUzLRVn/jC84Z/sj+aCJNntaCydOnDDGJIPbcDnJ9vfaa67TFOH4Gpts//P8N0Xy18z/kOMDH8mUImkHeiRnhQ4PVmR0e2n7lfkAS/3lyeNY2bF1Goh2XP2hkPudeazhpZEeH3/FAqPhnxseCdOmTdObLXqd3luZIvyLTX/Gv7jyl/mf+T/Z9m9oaCgMDg6GG2+4Ad6I7lGnzP4eOvT7cNNNc2Wb6QoZOoyqemDbEGiPyym03273NUUgWyXMk6kz7+QVzbrt91Qz4Ry+/dPVNQ0XyujI/o9X/0bOnQNruzB3eM81I7FjU4I/WfPfeOm/nPHPnBkKb/1xMNww9wMQmQtkf0bw6+xdUj4TfYqqiSdjHkeKEslOc3F4yYMpdRV4rQQGyC3dodPhoaK7qqs1pLXCP3ToUJg796awbv23w6oVq1R3KvGdEvb3YtCf8V20Mv+z/EEbYDveCfp/Fob9KmwM3rBhY/ibRx9taf8upP4PnxsJD+IrzE9veTqZaz5iX778r/V9Hhp23M9C7GLEO2OCKLuPbB3/s29f2P7ssymNo7Rl65Zw/xceQCP4OaK4Qtmp/WfjxOjU/h46NBDm3vTBsO7ba8Oq1SumHD8xdILz33jpv9zxh86cgX7MCN+nfjz2WMfj3w79xR4fSZitvbigaf8NF33wrQIKszky1qyumQRHiL+Kq+r0iZgRM5PQegKyGg8rg9QG/BPHT4SZM2eGe+5ZHLY9u81akCBNDT77czHpz/iZ/1n+cJN0kezPhda/7du3h3vvvTds2rwZX0D+ynn270Lj0w5Xq9VwxbQrZHy5BZP3tHxTrlP7z7vz973vevumDZbl58//WHj55ZdJgrU1Dvs/XvpP4G2z62ZeF5YsXhKe2c55I80w6M35R8pV5NKZ/8ZLv1g9CfPvxcbfvm17uO+++8KmTZvDgw89aBI0ifN/BQs+sK8cdeoeDE2UBAssXZ4MounLzWRsXL2JFRWwd7F6irCYek2FiitLLMwNabyDs4CF6vF9FcjfImDDU4lv3bZ+XQz6Mz65nvkvxYiKleXf5la3J7YCJMNRZpOy3RCN1/5Mhf4t+9KXwtNPPx0GBg6GG2+8EV2eWvtLGru6bL8YnWwa4xGsBHXh8VAn9B8aOGSPz9ge/v5548bw2KOPmdm0htR2J/a/E/yymZgGOngrXoNTR0G4lMd/tPlvvPQ7uy8H+R+L/i8tW6YVyYGD+GL4DTdO9vyvDzSY4SC34FXV4P3wgVRiHnJxZQckSXeifkn5grSL4cUJAlf8aq9XRtH4Y4vuWbORNFQWB/66f1yHH8X7RnjtN/vCR/7sI3UlKMkXGv9i05/xL678Zf6/M/l/DJt6r7v6mmivZOGa2p+pGn9OzlxxIZ6+k0NLN4X2l7Z0GvbeEF9GFtyg41OZRt60b/9//wfbkuCNrFu3LqxcuQLzmk8MaBhHu/Z/vPz/zrr1YfU3Vod9ePT24Q9/aMrxbdHA5rOJzH/jpX+i+CdOHMfHKq+V99rJ+KdhFumTR391pBqmXYG9YVwgoSPLY5Lm/2JzsxTOHBy0DSwA6RV0gBGUX5gzmhCALYijinwaKx/Cgf4DYc+re0N3d7fS2U97wmtVZ+HR1bxbPhTeffVVyAExqGh3AQgj/lk835sxY0bo6ekN/fv3G8AY+NuwLDZ7drdA2CduHuyeNSvccccdwj8Fg/f8Cy/gy6Msw07XwttvHw133313mD59Ougp8JFL3o6LfvKHzfOIQR39Sov4LNGM/qnCP3Dg9fDb3/5Wew2c/iNH3w6f+fSnw7vedaXo37njF0ZI5P/Q6aFw/fuvD7f9xceajv/lRP/F5v9k4b9+4ADG8X/1TNz5zx+V5DheeeWVkr9f7NzJLJNJyN/QmdPh+uvfH2677bbz9G+q5G+y6C/6CwLH0L+uyrTQt+ZbYU1fn7jRif79cfCt8MPNPwzP/se/hz2v7AmzYF/+6vOfD3/79a9jT8x1eryz/3ewV/Ylzrb1/6mnngpffeirYcuWLeELD9yvelNlf+xRFzcdwxbhH3l5bgSOD2ayTuz/AFZ8PsgN06jPRtauXRdWr8KeTBxl+8/23f6Wx7//QH94dc+rYRZseDGexfwzE78JNg+OzLuverfaZ08b55+zZ+O80dsT9u/vVzuEGw1/27ZtmDfw46ukHXpxGnuvOG98/ON3iP4Tx0+FF198IXDe8vnvbdjIuz/7uTBdDqu6E+cL0nb52P8y/53+Lsjut6AbfX1rOhr/ETgozz//n3gstSn88r9+GY5hbr3zzjvDw8uXh8989u7wT9/7Xrjp5pvDA/ff33T8HV9TI9kIPrr8PfXUD8NXHnoobNWeMdSHgDQbf/hGzGh//oVnWquNwATYCSGueSioplRPLrJUyYrGKvte21dbtHAR+g+xpC6pK+oL97fBjdJmodrSLy5VPTvV469YsYJk1zZv2ozs9vCXL19OnqkeVFb4GzdsSM33/66/tnTZ0oTPft1++/zawMCAMCaLfvKoqn9sNjKFgbPK0/xaPaynPzI+Bu3R7812go+7IvGLvNZYYbyWLl1a27fvNfWKbW3cuCGNoY1lpdbX18cs9K8gYjz4BqKGJk3+OqH/nYJv4xh1DGNJ+dc4vlYaxw0b6/SPY8lxtOPiyN/F4H9vzy01rEYU+qhOjE7/y3v21ODkRD2o1Ghrntm2rUb7Qj7yZlQh4uORv1OnTib9S+PBiClZMh12XSoxCfoHxwd9L/Sfcfz6ehmkLfyDBwfq7O9a8tiNAulwU+Fpfi2kam3fb/bVFi5aaH0BH9kP2nOz6cU8snTpslgDQQP9K33e2Lwpsk4MND46XgM+x7KRfto8B+nv31/7Imyi2T7r1/z5t9cOct5owPc6l4v9NyJ5LuQfiw3Qj7WW1Yb8jYyM1Gy+trFiffKP+rF48eLEN44jnExAxTHx8RBSgV8CtnFD306cOpXasXzPKhrxZklKu/yXto6wBo7YLfVPcZ5ipBRVopJZacRi6Rq96OnpkTLT0cGrjCylNjdshAGGoaBjtHTpFxOq4/MNMxeyNw8fNmw1THJSVxRLeBH/pR07Ut3nnnuOkJJNq1mr7dq1W/k0YifBTGqjtUHnwmOqNmH6rZV6fA6Oo1hYoBaxqcfv7najztX28/EpzHg2UMMmTMtXIecqyjcZ/9hMHf8vVfo5KheT/5OF3z27GMdm/JdOwgBt9omhRPU7gX6X3WRcwQTOTS6plD/ygIbdyhZUFzHjnMsqHUibGLtqnMxpn1wJHG/xPTTwldqSJYuLynEAGvGtjveoQHXHatfu3apZgqnDU/14cvyJ6B/pcceNdpdOMyezTvF5A4lFomR/5VyCknbpd07cQluDfvAPK09GOwjdyHmDzhDmDo4JM8r004Fz/MNvvhm53x7+jh0vJcyf+ryBxtU+Trs5b8D+zZrVbfNGzFBApHeQ/SP/OXbtjP/+/n7N5ZzPyRs5g+QHK8djPxYc5LxWKrXBwSNxzHzsfNTHtr+zumdr/KUfk8R/PjtTN2N7iHvHGaKTpUt22cp55710cT0yMgwFMuFdsWJlZIEHI3WeoAm3AfDMFSOscckxsjsPtNsBvpQD2EvgbbKjXvUwnCgqze3z56f22K7yJ5n+5FB5+066QjMqjF4q+Fu3bDGjAsNyBMJZcK0qPlKwd9Mgi56xx/9yo98FwmRBI8MTDqZ0Jn+q5g7FFI//lq1b4ziG2uARjGMJn3dflP/du3/l5F4y8ucdmgr+9/b01PAYRsM0lv4tXLgwGu1Q24Gbqlbyf3poSBMn+T/e8edkzptB2S3JHVu68PJX3GiazeUklVZ8OsDH5mzxwO2vHJ+S/EXGIGht/zgX+CTJ1ZtG+tMKAuYHfKOnjktcpTaHiY7bsOV1gG/9Pp//hw+/qcl9PuYNNkrrx1GhzCqMvbQcZvCIE3oH+Kxl7aZYTJla+0P9SCs+6lFz/IGDGG/YE45XL5yl0eh/RKtqWF/R0Xr8R6PfbkCCfAcrN3H+e4/EeTbKJsudcAOhVGWwUIpYWZ7tfw3Pv2WAKYiv8bEJPGL9iytTXGKnoJFxI8PFchXrW16X7sx4rd7EdtvBX5aWJW3rEusceWtQRqWnt4cNxoON468Ez0tLFZJdIKVT+r0dAqmlBvqtA5cO/vHjx81oYDxovNln9m75I7YMzJUy54siIowpisQkXNv/IkT25UC/0aHOihan3wjDVaRLtCSyU8SK8RzLpXCK6T9+7Lju2Kl3Pgmzz8sf5mPgSm3Xr2w1wehl//H3/0z+zbDT8Rmdfq7u2Bc6KnKUrDTOjPAo2T9fNTn85mHLLhcTj02iSk9G2ID9Rf7zMQDtIf+sdCzCq9ge01UNWWV8tqQMz/ZQqbgYxf5Uz2GFnbiQGcfnCr3ajKd28N3x4YoR29Kjrjbwy/KneUN96cK8sS/SWtDPuYFyLOcMjo8d4kqcN+Ik3AH9jq+JtYH/vAnkPMV5g6y4EPx3fNES+02cyHpDLaVbBpNVwnulkHlery53lPFvxO/pwY0BV3zQop8ax394eLiQF4z1WVyrcAt8PhKmczSa/BPO+9+Mfj46c+fUytZRmCp7v+tyW9AfTPcisyMnvKJADKmICgYlIorqMx4HY/nyR3R3yTtMXzYtVyZzXUGYX8bvxpIZFYfPDeuP+h6pt03weWcmBgGbguuTOpeS/XBK/bqM7zNBPRpL1qe0wrc2WRZ/sUqSUctEcn1bjs87nmo1PhaMZRlgozbO9XUmG5/GhH/cn8XD90zpTlcpFxaf9BmCc0OgpdPY+LoLFLNZFn+xyunT5F9xOJKnOKIV9yvP9TA2Fi8nm/+TQT/1D+8mS/4XYb8E6bc9DHHFotx3JwuhU3yp0C+b0Kg0IMb0wDo+Xv5zElvrexgiDxrpP4LVMtog1wlHjMUVlPHPoa+zurth2K0lEzxw0xjqZjFVbyZ/dByIR7tI/Fg11UmNxZQyvlBZIfFMF6lKSi7X9Tgyy7SmR10xvwjqe9SIj9eN0X84PVH+tGoQq4yGz/ad/5RVnxf0JKAARwzbJzB2ZefMmGR7IMl/4q9YyScM7dNPCOLzcVeZ/8dwM8hrbHRmERyj02+QXqZzfLZvtZ0bhlqcvW1LKfP/T5wfBOllqrXh4bO14bN0SJDlyVY1IcXLxH8Wo4OiR12emcKikbTyBv4899xPrT6zE5AurE9I3rJlqx5VelNOqV87xYbgV55roRxrzOkcf9MPlFYFG/9G+pVpDRbdik06PlZ8MOEanoWskIhARgRIjaXr+s7xio1SYKhMXCouDi7/VWs7Xir24SxCvpqK+DR4rMu/tDGZbE14iDCerovWPXZ66HTCp4PFx2YmvIbPuiLc6VXFyaXf+iIU7xbC5vhcJmc/ZTQwqKTdaMSGP998jHRuzFbGGPQXqEXMOtEcX6xE3x7BqgCFivgcF8Ztn1Rn/C9Qi1g7+OOVP06GGmf0mzLHPzuqumvk+JMm20+mwb+kxt/5P176I7EpcIfV9Y/0+zhqRMQCnC4R+Xf6T/8J49hLPTAZpPzZMWKPv7u4aTvu71Alq5kIj5FC6oqYt8MUyoocn1Hod/mnTu7Y8WLJ3gCTsBPAZ131rAH/4BsDSf+4p2Qi9tfobU5/I7496rLHXK7/eJ29Y3zu8XH94xgW+6jiwLSwf2Il8wDp+OR/I/10THz7hM0rVpOtc95w7A2+Mdm47OAsZSkt+E/5c/xeyAjjvAmXogiKJ1VWEE+l9i1acL2IWc7o+J3q/8bvb5SusJ+kPe1pRR/5uNT5wRv/TvBJ+zo+Cm4x/9sqGHQUDi7HufEoqC5ineCLD2K1mI6qlMUaXkI6mMZf+sHEJkeBWsSs2Pn8j6+zY51E3OKYW0wpZKHSccVk8pn/caFLXNh3m/HSILrGV/3mzJkNk1HRh4ceeACfLI/Vdv58R1iwYBFWkPmBqWrAElmYznf0cS0YyD4/pkWgdWvxExWrVgsrVm8Ln5265Zbe8Hr/66hWCzPxaiI8RCQjQ6uw7LURZZQybviWMzr9e/fuCceOHmtJfxkGDA9n8Gr+XXfdZZAt8Pla/Y/wEbMnvvY4370M2E8T5s27Ba9ZziHpOqDseK3yRcTJfPxvwf9G/IoN2qj4pH/P3r1h/u0fEz4Zve2Zn4QlS+61eobaNv+dTNLfiL9376vh2LGjoqkd/vP1efJPo8QBakL/IPi39ekfhScef0LloBhh3ofmhTl6TVWfpAh3LlwE/j2P+hMb/2b4ZfkfL/9Fn6gbXf7Gwt+LT0nwq7ly98ANLBFjHJfYOPrAiEtMolYYcqf4Q2fPhp07duCzE3xdnt9tN/1vRT/H8aO3/jk+0jfXxlDnevyjeAX/RxzHJ76m/u7evQufvpgXZsOe8GDpBdSDF54ft/z39s4Ljz/2aFi5amVT+ofPDetTDsYivGSrBZHIvhby1w79xuaCy6SnzP8joH3Oe0An9H/d+nVh1cqVotj6Yfh8XRsOQJhx5Yy26D99+nS49dZb9WHEVvggEDZ3OrAADNPLAI4E0kgs/thlHOr5KPT/4dDBcNPcD1pxlFu/dn1Yoe/4qFJsoD36t27dGu6//wtANfyd+BTDgoULNP7k2fDZoTANnyCxlvHBRTR7Bb5FRFn+Nr4ftHolf9oIkE3sjzIiUWX+s2ecN/g5Fhq6WTM5bwyiGUdRsnEChTuxv5wDcMMfZlx1pdHUAp9IfK37zNCZ8NGPRl1hx5Rej08/8b9//Wt8+fu+cOBAP0pgTsUnRz75yUXhJciI6z9eEArvfe97x+Q/YYjf29sbHnv8sabyx6lkzZonw5NPPgkW1fD5BRunyZB/x9dr7epJTGGAg/rxnjlzwB36Bv8QVq5erVIkbLz4ctvo4FWju0VfSg4fuFt3sExKiEtM6ZqRKt4Y2cy+obvgDEJuCkuvKeIavNMGJXrp1pi1SLzBwbesHsrgN2zk7DU0PyY+y/veFOKfPIm3t2KnFcS42i0ltEu/0WZ0iEZynvTqjQbzgq2M0Y8xw92MgZbgjKxSAvGX4S03b5N8WvF39rjvlN5AQxWUUZXY+cbhSbkqZyU1jrGSghhvpP8slkVtmdle11W9RgDULao3H39v1/3tRnzSRZ6QTiiS5IEh0xyfcS/HPL6xUbRrUfaksXt81VX11B6WvfG4lFVPUQZwqJWCgLoElutE/oewstiIHxtUwJVHYRIvYiqIcT22KSV0is9Gm+Hr+Xukn6s/Yl0TfOucemhRlOmE/n37uJkUMs6xJN4o8m95Fb0V6LwYDd82+ppMcPxto6uNYyQF1ZvT7+22kr/eHm7etM3N1hdr0fnvb/hQjuo/uSHIUvfHh19qIMqF4eM7KEn+tceCyQ0DbC9+RL6Q723wf9OmzRGnDk5sYl845o36Z4/cz8dXeauJcz39tuJT2D9/nNiu/LGfZf3XvIGVH/bNbEGX5o3iEViBr0eTUf749pf1sxhXXvNQEOONCeznI8sfTjzVvNHAf2tXNdVaY3ZEOA9f4xbnQ7drY9k/jVsjAPpYdN/o59zg+se3b9k+9/pxnxZtTLv8d/lnG63kj+Cz4hvAxDmaVpMKnjhfW+lfiYC6AXF8JpJG9btEP+cAtzWmv8X4O3rijvhknFI7FjXoGGcdOD6lK6aUC/LCs9UKLnnNk9LRO79GMr9xYBN/0Ovj+JGxGj4WpuUzGhU+2uGh6qqvK6X19/eLOAr7Yrwaatk4ezkBW91W+G8dGUz4ZNTuXbusutrw4WiOr04U3WmKT2GiUpw6eVIT6qlTJyzkNYTwBL7JoXzEGaa3D9rA34PvhWjSB/09vT1N8dU9ttWC/1rQi/wSu3CK0KwU2zyffu0FAS7x+Xq7cPwc2xMmW5kAPseffCJvnE/8jgnT0h/5yzL40xsmbeIb/8zh7OkB/2I/VT2eyk0p32lUGE+xkAU4lyuhCPcT8JbHltzr5Z/8d/lXfhP+e77vpyoA2sN3uqxf9fgPY3+dt699bU3wicI21I4gncD28bmfhQ7lWPJPPeE4mvPZWv4isoK0yRWvEPdiHBv5Pxr9Y8k/5UJ7GFrQzzzaH/LwmW3b0R8WdL5YZCL45abK/JdhJy70b310zCJawhfPqRfke5v2h4OsduKpEd8mlJIDC3w96jLK26Zfb/mgrpwx0OE8HgvfOFvVDbLLLR9l0IHBR/TAi7XaHmE3CpEjDMg8XY7U+vfbvEHe3bN4SZJrHycViycFXt0v0JCcp8h/jj/fYrVsnL1cHDBv1/EVxrxm8sfHiaYrURfiGI5m/8iXdvG7Z82W3JD+3+x7bVz0E48H9aOV/HEuk+PG5R78tUu/6IinMisjy4DqqepCurRUnPkfheUsgka9lcnKKlBv/5rxX8XiSQEREeGP0ulAE7rLYOXYamKiCjBbha2G17PSI9pMRebzWWzfmr5YBbmpoEe8NWYVxvCN9JzYPpLHUgVzrM5o+P78Uc+CJcS2etEu/kTpF451MEGKeCe7kRjSp39G2xnfLY+7gz17XlFiY5XR6B8vPlfIKNAV7KPg+FHA6IzwmAp8Z89E+c/NfC5/r8CJtAOtO0AjMaSvxP928PGL1poUsQQuPnHlks3r/gORkeFzkn8z4vFjcCV8ljfjAX5DRtMbNGijHfzRxt9WOk3/HF/jWMI3nhTnTukfDV+MmID8O/1nsDHT5DDUXsEbouVjovg07HbHaK020r+RH3rU2AZ93LRRZCaKX6aFccenYXf9szeibNAuNP4IVny4IlfW/2F9d60z/IMDb5RWjjA5+aoahWIM+eNbQY7f960+6AErtI/P78doUoQ+sT4Ph1TMLxqZqXJVffrB8d3+8UZQ7XhdXY0+/wl0EuS/U/r7sLDg9J/3TSF2ymloQb9nU/+oH63kz5wPuym4HQscXo+h27+J4tMINqM/YWN+8tftJ4r/fwAAAP//5g4XMgAAI2VJREFU7V1NlFTHda4eJBkJWQbnWM4JyJm2vYwEmyQLy7AwthdB2dlHcHIO8sJgIcfJMSDLsSyQsogByZswSJFzGDmOHPBKwjvwCpxFwDnHsIlYeNoWI8dCYvibAcT8dL7vu3XrVTfdMz1/iJxTBf1evapb96t769atelXVPaE51WzaxyN4ZkJ8nOJdzzFNT1XcCJvNU6dONWu1WjOE0BwaalTlxQiPKjLVnLQIuMSS5I/P8LlzKGvl9+3dU5Vn/gz4IyMjzT7gEp9h08aNqgfrMtUjvioYaQ2vgs3xJycnIcNkc2oSnCHLFOO6+zNkZB4+kxPI6xF/Cnxd/p8dPjQr+SV0dqGKBYv7dPhbtmyRno68eaQ5Njqa8I8dO1qV70H/GbSi0+GPT0wk3dEWpEfpi3p0XeLu+usRn23g9nfo0OGq/j3qH+BRaRRBirNbG369v18627Bhg2Q1oBjFrR/5Ndid8sVGDMSebcF8tjPznbUiPeITSW1qbPXEdqT9HzmCdhwbEz5t/9jRY0ZLqliXbv2vV/kdvxf7VxuiTdlnesVnO6ovo/6HDx+2alFBxoDwt8ivxOxCUhZRMUWseL2/3tyzJ/qWSOJ05D8wsF9tS/yzb5218uQjRqwD47xV8ZRoWZZPuvgRLR9VpLP/o57YXrSLvXv3WmEvz7vwwIn9BIx68T/0PVZNMeiIT1sgbi36XeKzrWJBh034kklPEgYxhqnm8PBw8r/kt2cPZPD6x0Ld5D91EuNGxG80howly3h53vUc0/RUxYeHOW4E4e/dR92BOpY1NmLQUf4LGDdcfhJsjOMG279XfCMUbLq046vN6M/QFybYF1I7xjbls/qI9RWTl4Lgv6ofIyYKkvlscId/dijJ7xVox2c601huuv7fj/7Rzf4m4bdNV0E+zPFTRRw8Yik/YvaKX/GK8vGmD8dG9o+g+uXyV2WqCvQiP2YGUEYlRVRQZJI42DPqkFFmQIjaZMMmLmxoVY4kaOz2QuIj3sw3fHY4E67W3KPOb3l54U74V65ciQ2CTovGYTh27Fgz1PrwqTWvXL6Us1B+J3xl4MJqMV9BdUxPzdWr16Q6yghqldOwzmvyc+Bz/MmJ8YxhZIsbdeQY1P/g4GDinQbVNnyWqGpjxXm1dFydp9IiZRf9b9++XQPk4MGDqf1d/4uFv3r1ashYa9b6zMlLT9KV6c3x7W6GPjlpbVrJyVhroKQHBw+KN8s+pknF9PK36985UoWxZNRneop6dsrqTgrrjDltjHfR/0Lh79i+Q3ZD+b393f42PIbJ2SLgc6Cjnms92L+1cV9z165dSWHSl9sq7rn/OXjQ+0GcPFatMW/9c9JJx94NX34jDsKqr+oY21FtzHZuDZxoih+vLpNoI2UP+h+HjbvNc9KQMDJ807n3k+hnZtD/rt27pm1/2qBw4Scdf0Iva1HGDJ8pJmfMy27nYA+5/9snHfcmvyYbEZ/1IWQn+bvh+7hB/L2c1MYw0/hz+fJl+T/KTf0z5O3PccXaM9Wmq/yk6Nb/h8+9Dd1iLIp21Yv/S31lBv1TXz5p5H3kwgXJwctM8nfyP+wf3eyPPHMZbILMVGuvTvKTZvzmB0ZgpBW9ZGPh1v7P5KRx0SCfcwPYOvHT3KCdH0s5T7J1Ll36X420cGAKU+DdRwRCqC9YRhPpGK6QmChEz9rUwhRMB6WWGC0GzfDzIz9HCS9D08ITQZpToMd8mqTA4N2wQkBDhCVLlgA3hB/8YF94+untRiBS59WKf+XqaPjYAw+AIgQoJ/RhRCXPa9euhWXL7lfJgYGBsO3JJ7viHz78s7By5Z+ERx99FJJML/9r/zYY3jv/vvgS0+SHINQNoF0m3sfGroVHP/e5sP6L6yXkdPJfv4763n9/WP3II+HMmdPih+YGw1q4ePFiWL5iebh542b4u7//Vnj2+8+FVagvQ67/XKeW5zojYav+d+zcGV566cUwOPha2Lx5s3TOMlu2bg3/+qNXhT85NYF0CkUc52UaYjs+//wL4Y033gjr138h/OCf9oS77r6LhNa2LWX4YPivDQ6G99+D/qAuo6WtgTef8bE40qZqaMNRtcn69etvwVcfEAuzv2tjN8Kyjy4LmFiF06dPy8VgMJXNXrx0MaxYvlzt/8sTx8O3/vZbYdVDnwqvvPxyWLlqJauW5J+p/aez/0omVEx1c53xoVX/Oe188HfsYDu+FAYPDoYnvvZE1GkI30A7/surP1It2K+k7hn6H4hntH+Xf/TqFbX/g5/8JHh3tn+ZQtTt2LWx8J1nvhs+cs/doLc+6raSyz8KugfQDx55ZA3a8deoEe2DrdIMl0YuhRUf/xhi8lCsLuKV/0E0yW95t+r/M/V6ePLJbWHn0zsTbY5/c2I8LL3nHuRRqqZ8ElYRFe/k//buezF85+mnYa8U1PA3/c2mcOin/xF+0xgK9T+tx/Tp2//3v38HNvkQcJsBq1zhK1/5KmxSGqQowh+9Mhpe+Mfnw4MPftKAdKX8oOvif777zDPhI0s/Ir6d/A9lXNKHtxDWnQIAn36U/b4dv5P8rv93hv83rPrUKjJR2Ltnb9ixc0ccM5AEvp3wCdlH/eLOcePNI0esdWU3rfJ3w2/CTy1ZcreqT9yd8G0MlM3K3Ip/dfRqeEDjRl+YQJsvWWJ+zu2PFdo/8M9h27an2nglD6H08YnJcPddKMv1JuqwqnLCJ9YLLzwfPsF2gx7YYhUtZGcay+LDOMeuZ77z3bB0KexQhGTrsrTib936jfDqq/DXDCh/5I0j4bG/fkw82O5LakuUPjEOGe+GjqYZf8miXq+HbU9tCzu27+zY/s+/sCu8AL9Pcx8cPBg2P/E1wNr4r6pm8o9Dr/fcfU/AZDKs/8IX1P6/OnUybPn6lvDr02cCVriwNkD5UUXKj5CkU1rFbPid4fDQqodEf+g/DoWvfvUrojdtdsavdAZSgNxqf2mW5FMtLnUyYM4Up15+90mU3eOcCjSNRoO2i0+teRSrLSyrgFuaeTGVzzGrovGE+PYBPjbjnR5fS5x9fcIcHx8XIrGMG7YU6v3UXHP58uVWlzZ8bqv4CkTL8h7ePy1Mj58ki9XnLccXDyR6jficyz8+frN548YNJbKOmzZuwux4XHVmvcfGRpt8+6VfOInlYJiH8qhrEzJyBgbfVKj/en+9yeXbSKBbOz4mN+IDw2WVhB8jWt4nNj+nTp1MbZXaDFgM6CCiWb1mjfRPembNRv6qjpEpOSjam/6pv+vSX1NtzBXHm0iTnqAzvoljQqB6ss7cBmLe9h22QsL4+fPnW+Tn9oDZBJZU9+3pKr/Vs9I/+S+E/Nyaoi77+/ubIyMXpsXXNiXsnzK24589+xZ9gnidRDt6yO1vvvpneTWXLnOXf/xm7Afgxn6wcePjTfZn2j11wXa0VSBsY0csB54tPpfy9+zZF9XhFc+YIrp9+7eFS/y1a9eKtt3+ia9tMej/ymWsDCCQCzXy1tmz0j1lYGLUktHwmYT2FG+2VYQJoXCHh99O+U7rd/JT0H3++udbv/oL9Oz4k5OodwRxXL93w+dqlPt/8mPfiSymlb/RaCT8tL0+B/9LOyH+7l27YxWjonBr1/+5c7ZayXpaG0ValsR/9j3yW7FieWqrTvJz/Aho/11xJZNciGXc7MoHT6E+yCfxipRVgohBNb3/s5UoHi05Kfwb12/AZm31nmOIti9hu41Gg+DaXqM8rOdM+JR9L4+ZKKDmKM/gd2JJ1zYrbp4+c0YYoiFdlPbKlcvqA1zNa5ef/pc+tioYQVhZRW+V39rMxj/amtFFzl7HDL/izZjzR5wQ8RGzN/yLT0pjJiVRqGJsDz75h9ksy8GnTmOBsqkUnhcRDfYtOwerSoVZ4fseKwdwCxmPiM890oNxW4hzbS59WZ2iwvBwc3wcTnSF6sNJpZa3XWLVGnWPz9rXjEukQsNFd1WginWTnxT2qfD1PI38bpzUFzsgOy0Dy8mwkOYOieeXlDfBZU2cn2o0Uv2oSZbhhNP1z8GDadxHzgMHecpKfRCDy4cM4hF1wUlrjs/BN4Wof7Y3JxkMLHv8xAnhv3v+D0YK2E74lumlQJNsDlxSXExVPjHzghGfvP1sAtvf68sFUwZ7Nr0y7vpjZzOcKdkH9WXbHhU+3jgTP+q/0RgST10yfNaBQbrTnXHDZ+WZ365/0ltgKeQnmSt8b3O2p03YHQkFIv67aEf1N8hG+akL8cvwrR3NUbC9XQcz4QsNlwq1ijk+Uzx1vvJTB27/bCvGyZz8XRdKx6LEyPsjSp8PPu2f5wfFo4P+mc5M+p8c/9gvjjXHro41L2P7g7r1SdlQYyirk7U/Vh3UPrNp/6GhIeCZ/HYukBVhUI0sugj210n/c8HnVpf7H8ohn9zmf0wIk4lSXb9+PU4yzP/xxcT1X0ldxaazPx2zgJ1z4CYTlmrXP/0djxO4/9NZL9LxX7QFGzeWmz+FLXIS4Hmd8PlSwRdN4WX9rxM+JbdAROBOY3/Mt1DFyF7jXg36gqzU85Ejb4oXfXCuf+ojl5/nbysf0B3fxsJsq1XcY1Wi/Z3UWV7Dp//hmM32Hx272mw0GmkStnbtuliw9Ub908/ORn7ydT+hM1KRpdoOcWpptv7XRlwySjqOETRMSkLM4p5HLdhhsBoaIncSWKtUo/jASsYqzaLtn8iV8IxqjxWKoUJ12EsZlsmiHIiFFRuejU98bGcZA1xP0AiQXhkCJwU2CAxjtt9SBzzYLDcexiOU16kH+UGSheohP7NAfu3y0yjdeFlXW/kxVuxslJ+DH98eLaBZAUaZGo0GWJr+HZ8ySGbKDf37nrWX5VmPlE8a8gev/PyMGxYxHJ90Pjs36aKMGb6dscJZKszyPcwkv1QsVpEfC6aoY0hrkaXHLW8Ke/IuD9tf+osMeMBReZDjrPRn+pcjYPH4qdfztxtLp25z+av95Fb8dv1bJa1ujM9V/jrbMdM/O7nzJneevepk/9bXDN/7H/mYjsz+1Y4kyT5sszN4a2MbcsDjYeI02YVxGUfWwOMxJWt/5lqoqHuVX2/NtEV8KFdLO2IC4W3MFSzjbtfz7/6heeL4cbogrdrZIWjXVXf9q6+Db9JBJmEVNQyupDk+66c6Sqe15sZNjzdvYOD2/pfLT/3vxyFpvpk2Gg3o9zRYgyfZtn+UwNWjAcnP9jci3lzn5O5xMuBja/+PiXbDda76p4zJJ8wC384fRXuD/HvSqgGrxLq3ys/BMvd/3v5zxbezOYbPAbVd/gsaN7w/2J34XLXzcOI4X+CQh/Tc/9EGuLtgmrcrz600hoaap09jxSOF2DZ4bsdvl1/MRF6ViQAoHdM66J+r9N7/B/bvT/bH/u+2Sl/v+FzRajRQT9hgwiR7/zgWZUAa7S+t+HTA97qx/3E1lJidxv+j/HJMClX7j42OqUxjqAH9NeCf31I/ESnrlEcy/ANoJ7ZNf539A2EB7D/uUVBVhqwrL6kijMc83Dw50bMSDCkv0uZpSsrSRR4NlMngQX50fN6AIxdspYNsFhO//9NobKz4JHmER0xGYlgE+bktowlKJj/R+MglRQ5EKdAI8KFuhhoNJaf6Qnc0fG6TbdjwGAzEvtlGPmTmdxdIz8q6Vf+xiJE6oRLtgWrwZMc/efK/ZJQJK1KIzun9wapEjqCaHz71R7k5eEaGCZl2pElDF3x3FO9zRYt1i/ZHPqPYWjn/7nlNIOlQFRah/TvJz3rxrVMTHAwgKSwSPgcoDtRH3nxTK0y0nV1cupdOEjqe1YC6WYwknnar/llSubj4PcZiKSvPXk+75RcAEj8vjIJsR+okx+eK44q4mssJj5wv+gW/hSWs/NqGT8fpk9lO+ndosTBmmhTyG6eNRqP5LuyCLyBGF++Z/bFu1CG35zjR5CDKt3GrkpdTcSRV9i9ayMAVCVHxYuSROJbFzZOTvjL8SKUyiju9P2RsWX4CZdNEGfisu/ebBERuLrPzEx8xRdakBi9NGrBdRx62kmpgFXSkJz+EXP68/yV6I4/EsSxunpzLTztxfJ+4i87p/UG4kSU40f7EEDIkfswmvcoYbbv8nOhxEo2zl5JfVJGVF1RxT/OHjO1c5aeNcUKT6hvbn/2IeTk+X/RZT5xxbElnfTvhc6VT/YP1VZ1JidCl/VmPdzEJoj6IRd/pBV1kvzPdjxv4WEY/Z4sR0+vf+9LBg4OpWu3yM8OqnF09TUlZOmqJra4sJB/jRMZerq2FsCqjPK9OpLFSbQX0iEue3PYIS7SZJN7ibelz8fFtlou3QIY7QH6zMVMSpWeMOuaKBZ18o9HAUxVy/dPIORO3UsYjUeoRlzy57XEu8ludhgwm8r6d+Ln8LttM+HzD3rplq3SRq8Pl1/45HPhlrIgwXxgthEmrllcBK2Mm/FQaPFvYAojt31+vp3Zk/mLh8+XCBgzb+uT2Gp2L14pyLCY+mZN/ClF+T+2G76t6PK92Dc6eg7effSCvbvrnxIdbUSnMET+VR0RtE6XwM1p8492ydUtOFkVqA8Sjb9tyksTttG79v1VRFesc32m6ya9KZArXyjN8Cvsw8Xnnt7pm0n+FbvLbV8ptVZ08bNUgA2IBPeKSJ7c9UplzlX8ttlaI7eNGK9DC49PX4sshLe3vsvWqf9Ln6piP/GqTyCzH93oaUBtg2yNp96h/GCPyYUw2ZkmCyS+zsb/Va1brHBbPJHFnhsxb2HZof9/p4Bk0vlC0h9ng54Ba8dHMlxxVC68KhPZoQrMEUwjjrCn+RzqrhPNhOjPyzBj31Jid41sn4sHCdYkvyGOw8guJ349ZbrW8Rxiv44cjfzd8myXz3EkDNbR/rv8L2QDGtx8XYS76T4WJ4apw9UfGjv/Yhr9qDuwfSHQL0f6zwXf5vZ694HNrh05eovGCT25/g69xOZlv3weTXEn8Nvnngu+8OuFzIuLbsjdutG+lsKRK4Wr/5ovPtuPS+dtYyvdVsGrLEhgG51VecHyJ06Z/l5H3bvhsH34oP88UUmcMM7U/HTtXIxg66b9KNIpu+N30/zi3cDAJY914pq6X/seVKk4+uf0zX/yZ5K90azF7mbIzNq7TtNU1jf7b5efhU59AU36fXPYiP2XO+19Vx+7t347PdvLttrXr1iYWi4V/HX2T+hoZeV9Ybiez1b/ae4Hkp8W24/McFfu3zvcoE2AxKMZLhq/+4QfTZ9H+vcg/OR634+B7p/O/qfEiPo8rUNfqH1IYKeyfJEb9e8E35UhqaSBbTzdI6cI5kUS05B4VKwKV1UXZ6TFm4panM26NYsbMDsd/7YEp/HDLxp2HkSEV/1PbkSiGLIoUPuFj/51Eqd3w+XadO0Nx+JDltw6bCwht8VwQnEpjqGFixmx/Y9QWxRzkj2x0k+yIdcIn61z/1BkxWYahcjJIsf+WwTx8uuk/EUU60vaC7+VIXwWV7opvZ8R4fqyz/fmh+aGhoVaWUaBcfieYDT4rRtPqhE+Z6aSk09tkf+xjW7jyhWDnJGpqq8XS/3Tysw6x9aZtf58cNhoNFtGWkvffxIE6Vq5dGGfb+RttJ/2TknT6zEH/1BkHfx445dK8b6k53272v3adrVTwbXY++MRJEoAReXlgvBO+bA6TRtqBzo7A/nR4dJby+6TDJz+c+PSC7/XzO8voM0t8L8/f79K4gS3JxcTneSDiMOQ4s9W/19vv85W/HV/nXeNLXqf2d1y/cxGAdrsY/f9/4gSG56Joazx31R46yW9nibB9jPNBksGJYuG56j+e8UHx3NjIVM/YB417iIbjA0aE85qwOP/Fu4qrAOmYw2CqVzS/iIQFjYpZXLWgYbEhFhu/v87lb/6aa4Wv6n2I8nfDp8Gcw9s5daqwSPrvhm9tOdXkYTO+ZTd+Y4c3eRCUb1oL1f4z4c9Ffr6dUX8c4PkWwW868M1DZ6lU8Tu//V3/c5G/vf9xoKX8OksH+ddhi5ROZgeWoRdD/+34BoIr1T4L/fOHGjnAMkzFbzpyW+lNfsMFvNzbWGuKefI/bH9OLFvCLPGNv3FPnhiPQ40hHIrtEz7P6ixfsQK2dlQTIaO+1f/pbRZtwIOqqnxeMQoDbovp/2ziY2/gmrSgT+tMFesxC/zGUEO2RPnJh1vJJrPJYPFb5SeMCNVwRqU0pVvZ2ch/HWcjiV/v719UfPaVjZs2NTc+zm+3op76D8uId1VfgpgMvC6U/XezP6a346ueWElkXXvBp0/f7f1jFu3fi/xbt34d/mUdFaGD7ewj3PKazv+ePftWtKcDKkdFz0b+aFysHkKr/aUVH8sEqQSOOBGGTwaoiDJbq+CllWVExInJUYeJtyWDQ1bMo47P2ap3InJdDPyt8U828EAev2InnLxSFWoVc5liisuiwumC1HnKb6xMag5Sa+JhSb618jzDYus/x2fc9c+thRqctRwlnAx/oZpx+waVlVLDL6D8OT4qojAb+fknRFRfbkOg7vqgzmx/D5FtstEKpS02B/y52H8batL/XOTvhH/ixHF9e8Xlp11RL6P40yVV8Fav2n+h8MXRlQ5Aj3r/tzrcis962sCKw7k4+Ez745t+awC3DvZ3E78ZxIHdsMDbQWeB7xVtt7+BgQPxG5BN9E/8jgnsi3Vl6KR//g4Vvz2UaERJ2qxSAPOnFIsJ7fixuHPpKL/zNhaYVKFfsJ70f7yz/bk9UoWEWtWkA75+0wiDpniADyfQnfTfjp+LGtnOW37/Vi+3cV15nfTPzLnir8FZFfoTfo3bdVnpjDGkdrC/2yG/1cPw/dA8tyJZpZnweVh5YmIiE6W39hfzrFQn+amvo0ft21779S1GHPzHypkH02NVR9aFZereh6Kmq5vVzcp56YzbDPqvxSL4BU2YLSwfWLrralFmxGT8AiK7iWjxS4s4HM3fUeV/RPlChf/oPZakNNImNh6Jd94YUN2O+C8PHAhPffMp/JrxmfDwnz0s/rcTX10Z9VdQnW+v/AUfmi/6vy32h16Lfo2h+A7q/73YP2n8F2AX2v/0gt/N/0WHKF8Z3Z2rNnxw44Ow9N57Zdr4ZidocHRTds6LUUd3s6j2f+wXvwhf+uKX5FdpZJQXX8XXLxb3is8yn/7Mp8Pvfvs71BwPkAUnrgJ+8yjcf/9HO8ofRdSNuN38/1z1f+DlA+GbT30TvxB8Gr8C/rDDuWrTnTIyLDT+Qo1/c5X//wM+pi3ot/Q3t+r/gw9uhnuXLkW71fBL4hP4nUi+IoAQClmw8d/nSJz+chJU7WzFKZHS/S2J1KDjVNmzKwbK4yO+YF2l5tNqUXjBikbEXfD5ex2H+McKK463Fd/08uHJX/Bpl0X/xf7dAdxe/7MY/Y+rTrt3PWd+LIlFO198/8ufgeAWQ73ez6FEH0y87K6pT00/8sevGts3bzr3P55z4YFTzGw4PqF8PCSdxddh+5u/MO4rDRTVpGwRlMnIWFj5WXf7faeq5wjGR5JqoBP8QuMbU4pV8F0Xveqf/YNb0jbP6Gx/qRkTc/UePfUy/9CKj79UoyMgTOETZ2KIteYxnzM0Tex1JwFn+Nz/YKDdc1ZmDxbvw9/40s9CZTM28SAt3xCMOl4LftF/sT92Bo5GrX3Dukjpf8X/0N3SDmbrf6/gb3798IcvhU984sGw7L770uq8WZb577Fr18MY/mbUl7/85fDIw1hpbwvE/fGPX9PfI7xv2X1d/f+162NhbPQa/n7Wt1FN/t0oGw+87horiv9v6+Nl/Lsd45++B+oe1h1t6lDR4PFrP5q4yAmnP1TKTJbgzd0z6NJyOdPxYRbyffktlWFGyje69BgjzrXgF/1z4ix7KPaHbhN1UfofHAdC8T+mB/rp4n9pEKaPNKCU8aeMvzYVcduwH8CAoWhQSdqBxSDe1EoNz/TYJGUKN76Ly55y28JMHl3O3DFmTb6KwwkUi8YS6Y71qMjTCFCi4EtNUJbrq+i/2F/pf/JDxf/AKcBVFv9bxp8y/i7Q/IO/A9OH2QnnMRpz0/SEI7FPWZArAlLYFIePDD5Op8JKjbmaSPkfmweLOBMiGqc64oa0gl/0L/OS7bjN2YOl46pIsb/S/4r/4fDP7sBQ/C+UYE5C+rBL1E4Zf2AgZfz1FfJ8/lGd8ZHx+IQED/zP/Vf4mWZf64qMSKOZca0H30kw26NPYk9Ub3R7zKljoeyWchUp+DYhhDL4v+i/2F/pf8X/FP8bJ3gYWPA/jRkYR8r4U8bfucw/ajx0bof6aUSY6ESrshuvDGZtWmp1q6v2scwSK7JopLFYRs8l68QxlrebpRb8ov9if6m7oR+l3qI+Vvof1joyfxL30Yv/aTWTefvfCXyFeMldd/Vsf79t/FZfg3/xxRdlsXrvje1E/64KIVL8f9ajy/gHu4A+YB8f0vxDP5XPaQsCaqGzJbbu4FbstmskWJXhSoQeQI43Up6nE011gUCYiaYMI+aCkDkuX9lhes4d8YIPFRT9y8KiaeQWwp5S7K/0v+J/zKcutP8lv767+sKu53aF3bt2a9IyXf/bsX1neOmHL4b+/npoDA2B3qiL/y/j3508/leHmzXhkJ3HWRh6AH9blB5mir0BkTQQ2cqEtlA5dUE65kLKZxpDvGEjjDtsqSgiyEmdw98CcC/4phrqSvos+i/2V/pf8T9wCLfZ/5469avw2c/Ww4rlf9ST/8evVodnn/1euDhyUT4MNS7+n0pAKOPfnTn+Y+KDFR+toWOgzacomuT4Diqy4qSHjWlZoNdXJ32gtgyuVtDswRg3THL4yI9mPzHNoZCszIIP/bhSqKyoN+jSdnBNTe5NROGvesyKaqWei/5Nf8X+olFQHfyU/ld1FO9qUIuUU/yP/M/ExFQYHj4Xrl4dTb+U34v/4cTn+5j4jGjiU/pfGf/u/PFfEx/zA7b/yJUaDqQcZDWHoW9AhJsvMZqelcKdMqwGxSKVcwExx2bubXFhPvEEpaX4aXMO7vTLFY9ES6YMBb/ov9ifd4XUH0r/g0qK/1kw/zv8zjvh848+Gi5duhRGLl4Mk+MT4dVXfxTuW3av/Ht0xbI//sjhtief1Bhx4JVXwrPf/4cwcuFi8f/pLbSMf3f0+M+f6ubKjCYu1QU2brOO9Lcx8JivJvg5CxUBrU1m2DXihEb06CNMSgF5/CE6JFaTGxAW/KJ/2Igm2nbBEy2LdoJpM41I9sQ0e5so9sfXCdMSe2/pf6aP4n/m53/r+Ltbu597Lmx+YnPAH6wMP/nJ62EZfp25vf+NXb8enti8Wb775QMD4XvfezZcxGSp6H9++o9OMPbs4v8Wy/9jxUdTkGjY0DctXIMLb3CoOpNj6fprLPK2oImjkTlcPPsZHYtGp4xcLOfwqBC30+LOGB6IgaAlZotax2KceRzceCv4Rf8wONoc7KLYX9RFeqv0CY91OnVVi5b+J4sp/mc2/vfGzQ/wxyHvCxdG3g8fX76iZ//rEx+uEtFv+5Ah6yz+v4x/d+D4X+OfCuWv8NgExjadNOfgWJNCa4qoo5fVHjCzOWHxV3M9mpNOPjrycqT4GLe5PLXgc9OvVdtSJi4a/aW2on+fBMUzUMX+YBel/xX/Qx+hzpBmH7Pxv//5y+Ph859fh91DjgYhvPfee+HBB/8YMfjlyv0kCP7l7NC3JLyiw83PYntsZF74tTQatMIZYFWB4v+K/7OX4Ln7f6z4YOozhe0nzMp0Xlb25U40Tte1/EKbjsZnyzFIaA0+faGh2kTK87n8x1UcLMqjbC2u9LCL0tgLftF/sb/S/4r/gT/8EP3vunXrwqqVK+Xmf/r6T+Gde/P/Bw4cwFYXDjdj4mMbjsX/l/HPLMFmAHfe+K+tLpuA5NOVmMIbgp68Q+LBz1cwx079Q7D41XefE3EHTX9jJzGIPMWNJakYS6ty3FxiCm8Ieir4pgkoo+jfO1Wxv9L/+Lutxf8shP9ds2ZNOH3mTBg+93ZYuXKVOd4Z/O/JX/13+Ms//wv58sc3bgyv//vreImGZy/+v4x/NouPA3gc0++Q8d8mPppgwIWiopx6sG6awHBvzoMGXF+C9AHHM3mPgokuTmvIh1nG0ngqwcmtDJdj+XZR8Iv+i/2V/lf8T/G/ZfzBkFjGX04MLGhesXDzj/8Dk6SOuhUsupUAAAAASUVORK5CYII=\"/\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e(5)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003eBut in our method, based on equation (1), (3), (4), we can calculate the probability straight.\u003c/p\u003e\n\u003ctable\u003e\u003ctbody\u003e\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003cdiv class=\"embedded\" id=\"_1555413220\"/\u003e\n\u003cimg 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\"/\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e(6)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003ewhere \u003cspan style=\"font-family:serif\"\u003e\u003ci\u003eF\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e(\u003ci\u003ex\u003c/i\u003e)\u003c/span\u003e and \u003cspan style=\"font-family:serif\"\u003e\u003ci\u003ef\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e(\u003ci\u003ex\u003c/i\u003e)\u003c/span\u003e are the distribution function and density function of \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eX\u003csub\u003ei\u003c/sub\u003e \u003c/i\u003e\u003c/span\u003erespectively. \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eΦ\u003c/i\u003e(\u003ci\u003ex\u003c/i\u003e)\u003c/span\u003e and \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eφ\u003c/i\u003e(\u003ci\u003ex\u003c/i\u003e)\u003c/span\u003e are the distribution function and density function of standard normal distribution respectively. \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eΦ\u003csub\u003en\u003c/sub\u003e\u003c/i\u003e(\u003ci\u003ex\u003c/i\u003e)\u003c/span\u003e and \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eφ\u003csub\u003en\u003c/sub\u003e\u003c/i\u003e(\u003ci\u003ex\u003c/i\u003e)\u003c/span\u003e are the joint distribution function and joint density function of \u003ci style=\"font-family:serif;\"\u003en\u003c/i\u003e dimensional normal distribution respectively. \u003c/p\u003e\u003cp\u003eTake note that \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eφ\u003csub\u003en\u003c/sub\u003e\u003c/i\u003e(\u003ci\u003ex\u003c/i\u003e)\u003c/span\u003e can be expressed as\u003c/p\u003e\n\u003ctable\u003e\u003ctbody\u003e\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003cdiv class=\"embedded\" id=\"_1555411585\"/\u003e\n\u003cimg 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\"/\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e(7)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003ewhere \u003cspan style=\"font-family:serif;\"\u003e\u003cb\u003e\u003ci\u003eΣ\u003c/i\u003e\u003c/b\u003e = diag(\u003ci\u003eσ\u003c/i\u003e\u003csub\u003e1\u003c/sub\u003e, … ,\u003ci\u003eσ\u003csub\u003en\u003c/sub\u003e\u003c/i\u003e)\u003c/span\u003e is standard deviation matrix, \u003cspan style=\"font-family:serif;\"\u003e\u003cb\u003e\u003ci\u003eμ\u003c/i\u003e\u003c/b\u003e = (\u003ci\u003eμ\u003c/i\u003e\u003csub\u003e1\u003c/sub\u003e, … , \u003ci\u003eμ\u003csub\u003en\u003c/sub\u003e\u003c/i\u003e)\u003c/span\u003e is mean vector,\u003cspan style=\"font-family:serif;\"\u003e\u003cb\u003e\u003ci\u003e R\u003c/i\u003e\u003c/b\u003e\u003c/span\u003e is correlation coefficient matrix and \u003cspan style=\"font-family:serif;\"\u003e\u003cb\u003e\u003ci\u003ex\u003c/i\u003e\u003c/b\u003e = (\u003ci\u003ex\u003c/i\u003e\u003csub\u003e1\u003c/sub\u003e, \u003ci\u003ex\u003c/i\u003e\u003csub\u003e2\u003c/sub\u003e, … , \u003ci\u003ex\u003csub\u003en\u003c/sub\u003e\u003c/i\u003e)\u003c/span\u003e. \u003c/p\u003e\n\u003cp\u003eAs \u003cspan style=\"font-family:serif;\"\u003e\u003cb\u003e\u003ci\u003eR\u003c/i\u003e\u003c/b\u003e \u003c/span\u003e is real symmetric matrix, it must exist orthogonal matrix \u003cspan style=\"font-family:serif;\"\u003e\u003cb\u003e\u003ci\u003eP\u003c/i\u003e\u003c/b\u003e = (\u003cb\u003e\u003ci\u003eα\u003c/i\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/b\u003e, \u003cb\u003e\u003ci\u003eα\u003c/i\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/b\u003e, … , \u003cb\u003e\u003ci\u003eα\u003csub\u003en\u003c/sub\u003e\u003c/i\u003e\u003c/b\u003e)\u003c/span\u003e, which makes\u003c/p\u003e\n\u003ctable\u003e\u003ctbody\u003e\u003ctr\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003cdiv class=\"embedded\" id=\"_1555411725\"/\u003e\u003cspan style=\"font-family:serif;\"\u003e\u003cb\u003e\u003ci\u003eP\u003csup\u003eT\u003c/sup\u003eRP\u003c/i\u003e\u003c/b\u003e = \u003cb\u003e\u003ci\u003eΛ\u003c/i\u003e\u003c/b\u003e = diag(\u003ci\u003eλ\u003c/i\u003e\u003csub\u003e1\u003c/sub\u003e, \u003ci\u003eλ\u003c/i\u003e\u003csub\u003e2\u003c/sub\u003e, …, \u003ci\u003eλ\u003c/i\u003e\u003csub\u003en\u003c/sub\u003e)\u003c/span\u003e\n\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e(8)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eand\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003cdiv class=\"embedded\" id=\"_1555411776\"/\u003e\u003cspan style=\"font-family:serif;\"\u003e\u003cb\u003e\u003ci\u003eR\u003csup\u003e-1\u003c/sup\u003e\u003c/i\u003e\u003c/b\u003e = \u003cb\u003e\u003ci\u003ePΛ\u003c/i\u003e\u003c/b\u003e\u003csup\u003e-1\u003c/sup\u003e\u003cb\u003e\u003ci\u003eP\u003c/i\u003e\u003c/b\u003e\u003csup\u003e\u003ci\u003eT\u003c/i\u003e\u003c/sup\u003e\u003c/span\u003e\n\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e(9)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003ewhere \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eλ\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e\u003c/span\u003e and \u003cspan style=\"font-family:serif;\"\u003e\u003cb\u003e\u003ci\u003eα\u003csub\u003ei\u003c/sub\u003e\u003c/i\u003e \u003c/b\u003e\u003c/span\u003eare the eigenvalues and corresponding eigenvectors of \u003cspan style=\"font-family:serif;\"\u003e\u003cb\u003e\u003ci\u003eR\u003c/i\u003e\u003c/b\u003e\u003c/span\u003e. \u003c/p\u003e\n\n\u003cp\u003eLet\u003c/p\u003e\n\u003ctable\u003e\u003ctbody\u003e\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003cdiv class=\"embedded\" id=\"_1555412288\"/\u003e\n\u003cimg 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\" alt=\"image13.wmf\"/\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e(11)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003ewhere \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eμ\u003c/i\u003e\u003csub\u003ei\u003c/sub\u003e\u003c/span\u003e and \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eσ\u003c/i\u003e\u003csub\u003ei\u003c/sub\u003e\u003c/span\u003e are the mean and the standard deviation of feature \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003ei\u003c/i\u003e\u003c/span\u003e in all samples respectively. \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eμ\u003c/i\u003e\u003csub\u003eki\u003c/sub\u003e\u003c/span\u003e and \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eσ\u003c/i\u003e\u003csub\u003eki\u003c/sub\u003e\u003c/span\u003e are the mean and the standard deviation of feature \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003ei\u003c/i\u003e\u003c/span\u003e in the samples belonging to class \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eCk\u003c/i\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003ePlug equation (7)-(11) into equation (6), and take logarithm, we can get\u003c/p\u003e\n\u003ctable\u003e\u003ctbody\u003e\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003cdiv class=\"embedded\" id=\"_1555412867\"/\u003e\n\u003cimg 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\" alt=\"image14.wmf\"/\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e(12)\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003eThen, the larger \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eHk\u003c/i\u003e\u003c/span\u003e is, the more probability that \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003ex\u003c/i\u003e = (\u003ci\u003ex\u003c/i\u003e\u003csub\u003e1\u003c/sub\u003e, \u003ci\u003ex\u003c/i\u003e\u003csub\u003e2\u003c/sub\u003e, … , \u003ci\u003ex\u003csub\u003en\u003c/sub\u003e\u003c/i\u003e)\u003c/span\u003e belongs to \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eC\u003csub\u003ek\u003c/sub\u003e\u003c/i\u003e\u003c/span\u003e. We can compare \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eH\u003c/i\u003e\u003csub\u003e0\u003c/sub\u003e\u003c/span\u003e and \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eH\u003c/i\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/span\u003e to determine whether \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003ex\u003c/i\u003e\u003c/span\u003e belongs to \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eC\u003c/i\u003e\u003csub\u003e0\u003c/sub\u003e\u003c/span\u003e or \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003eC\u003c/i\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/span\u003e, or we can plot receiver operating characteristic curve (ROC curve) and select the best threshold to determine the classification. \u003c/p\u003e\n\u003cp\u003e\u003cb\u003eDataset\u003c/b\u003e\u003c/p\u003e\n\u003cp\u003eWe collected 776 compounds from the Meta-CYP database, and each compound can be metabolized by at least one of the ten CYP isoforms: CYP1A1, 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1 and 3A4. The number of compounds metabolized by each CYP isoform and their distribution are shown in Table 4. We can see the total number is up to 1646, which means each compound will be metabolized by about two CYP isoforms on average. The set of 776 compounds were randomly divided into two data sets. The first set was composed of 600 samples, taken as the validation set for feature selection and model construction. The rest 176 samples constituted the independent test set for the model test. Meta-CYP database and the analysis tools based on our proposed methods are currently under construction by our laboratory, in which the information used in this paper was mainly form SuperCYP (\u003ca href=\"http://bioinformatics.charite.de/supercyp/index.php\"\u003e\u003cu\u003ehttp://bioinformatics.charite.de/supercyp/index.php\u003c/u\u003e\u003c/a\u003e) [41] , CYP450 Engineering Database (\u003ca href=\"https://cyped.biocatnet.de\"\u003e\u003cu\u003ehttps://cyped.biocatnet.de\u003c/u\u003e\u003c/a\u003e) 42[, \u003ca href=\"_ENREF_43\u0026quot; \\o \u0026quot;Sirim, 2009 #80\"\u003e43\u003c/a\u003e] and PubChem Compound (\u003ca href=\"https://www.ncbi.nlm.nih.gov/pccompound\"\u003e\u003cu\u003ehttps://www.ncbi.nlm.nih.gov/pccompound\u003c/u\u003e\u003c/a\u003e) [44] . The detailed information about this data set is available in Supplementary Material. In this paper, background samples were gotten from DrugBank database (\u003ca href=\"http://www.drugbank.ca\"\u003e\u003cu\u003ehttp://www.drugbank.ca\u003c/u\u003e\u003c/a\u003e) 45[, \u003ca href=\"_ENREF_46\u0026quot; \\o \u0026quot;Wishart, 2006 #78\"\u003e46\u003c/a\u003e] , including the approved, experimental and investigational drugs, and 7114 compounds in all. \u003c/p\u003e\n\u003cp\u003eIt should be noted that inorganic compounds and polypeptides are not including in our dataset, because the features describing these compounds are quite different from other small molecules. So the models constructed in this study are not suitable for the two types of compounds. \u003c/p\u003e\n\u003cp class=\"tA_Main_Text\"\u003e\u003cb\u003eMolecular descriptors calculation\u003c/b\u003e\u003c/p\u003e\n\u003cp class=\"tA_Main_Text\"\u003eWe employed the molecular descriptors representing various structural and physicochemical properties of the compounds including atomic composition, molecular size, shape, weight, hydrogen bond, hydrophobicity, acidity or basicity, charge distribution and energy of chemical bonds to construct our models. To accurately calculate these molecular descriptors, the chemical structures of all compounds were first drawn by SAMM, an in-house molecular mechanics and drug design package. Next, all structures were further energy minimized by the MMFF94 force field[47] . At last, the molecular descriptors were calculated based on the energy-minimized structures by SAMM. After removing the descriptors that were highly correlated or had the same value in almost all compounds, 176 molecular descriptors including 134 two-dimensional and 42 three-dimensional descriptors were gotten and would be subject to further feature selection. \u003c/p\u003e\n\u003cp\u003e\u003cb\u003eFeature selection \u003c/b\u003e\u003c/p\u003e\n\u003cp\u003eFeature selection is performed prior to the construction of the predictive models in order to eliminate the redundant features and identify the most relevant features [\u003ca href=\"_ENREF_48\u0026quot; \\o \u0026quot;Fox, 2006 #15\"\u003e48-50\u003c/a\u003e] . Minimum Redundancy Maximum Relevance (MRMR)[51] was applied to select the best features in this paper, which is a powerful method for getting useful information from complex systems through maximizing the mutual information between the selected features and the classification target, and in the meanwhile, minimizing the correlation of the features themselves[52] . In this work, we selected the top 20 features with the highest scores in the model construction.\u003c/p\u003e\n\u003cp\u003e\u003cb\u003eModel comparison and evaluation \u003c/b\u003e\u003c/p\u003e\n\u003cp\u003eBesides Improved Bayesian method, we also used naïve Bayesian method, k-nearest neighbors and support vector machine[53] to construct our classification models based on the validation set for model comparison. In naïve Bayesian method, the Laplace smoothing factor was 1. The parameters of k-nearest neighbors was \u003cspan style=\"font-family:serif;\"\u003e\u003ci\u003ek\u003c/i\u003e = 5\u003c/span\u003e, Euclidean distance and distance weighting function \u003cspan style=\"font-family:serif;\"\u003e= 1/\u003ci\u003ed\u003c/i\u003e\u003c/span\u003e. The kernel function in support vector machine was radial basis function and other parameters were optimized by LIBSVM[54] . \u003c/p\u003e\n\u003cp\u003eFor the constructed models, we would use AUC (area under the curve) of receiver operating characteristic curve (ROC) for model evaluation. Besides that, we would calculate specificity, sensitivity, accuracy as well.\u003c/p\u003e\n\u003cp\u003e\u003cdiv class=\"embedded\" id=\"_1555600311\"/\u003e\n\u003cimg 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\" alt=\"image15.wmf\"/\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u003cspan style=\"font-family:serif;\"\u003eTP, FP, TN, FN\u003c/span\u003e means true positive, false positive, true negative and false negative respectively. \u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we employed machine learning methods to predict the CYP isoform specificity of 776 compounds with their corresponding CYP enzymes, including CYP 1A1, 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1 and 3A4. The improved Bayesian method was proposed and applied in the model construction based on the features of structural and physicochemical properties of substrates. In comparison with the na\u0026iuml;ve Bayesian method, k-nearest neighbors and support vector machine, it was found that the predictive models built by our method achieved the best accuracy, which is up to 86% accuracy on average on the independent test. Using such a model, we can efficiently and reliably estimate the metabolic paths of a given compound involved in CYP-mediated metabolism of these ten CYPs. We believed that our proposed method will be potentially useful in large-scale \u003cem\u003ein silico\u003c/em\u003e drug screening and drug-drug interaction analysis.\u003c/p\u003e"},{"header":"Acknowledgements","content":"\u003cp\u003eThis work was supported by the fundings from National Key Research and Development Program of China (Contract No. 2016YFA0501703), and National Natural Science Foundation of China (Grant No. 31601074, 61872094, and 61832019).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGuengerich FP: \u003cstrong\u003eCytochrome P450s and other enzymes in drug metabolism and toxicity\u003c/strong\u003e. \u003cem\u003eAAPS J \u003c/em\u003e2006, \u003cstrong\u003e8\u003c/strong\u003e(1):E101-111.\u003c/li\u003e\n\u003cli\u003eTomaszewski P, Kubiak-Tomaszewska G, Pachecka J: \u003cstrong\u003eCytochrome P450 polymorphism--molecular, metabolic, and pharmacogenetic aspects. II. 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Boca Raton, FL: CRC Press; 1994.\u003c/li\u003e\n\u003cli\u003eHall LH, Kier LB: \u003cstrong\u003eThe Molecular Connectivity Chi Indexes and Kappa Shape Indexes in Structure\u003c/strong\u003e\u003cstrong\u003e‐Property Modeling\u003c/strong\u003e. In: \u003cem\u003eReviews in computational chemistry.\u003c/em\u003e vol. 2. New York: VCH Publishers; 1991: 367-422.\u003c/li\u003e\n\u003cli\u003eHall LH, Kier LB: \u003cstrong\u003eThe Nature of Structure-Activity Relationships and Their Relation to Molecular Connectivity\u003c/strong\u003e. \u003cem\u003eEur J Med Chem \u003c/em\u003e1997, \u003cstrong\u003e4\u003c/strong\u003e:307-312.\u003c/li\u003e\n\u003cli\u003eBalaban AT: \u003cstrong\u003eFive New Topological Indices for the Branching of Tree-Like Graphs\u003c/strong\u003e. \u003cem\u003eTheor Chim Acta \u003c/em\u003e1979, \u003cstrong\u003e53\u003c/strong\u003e:355-375.\u003c/li\u003e\n\u003cli\u003eGasteiger J, Marsili M: \u003cstrong\u003eIterative partial equalization of orbital electronegativity\u0026mdash;a rapid access to atomic charges\u003c/strong\u003e. \u003cem\u003eTetrahedron \u003c/em\u003e1980, \u003cstrong\u003e36\u003c/strong\u003e(22):3219-3228.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp style=\"line-height: 200%; text-align: left;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003eTable 1. The features selected by most models and their descriptions\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"border-collapse: collapse; border: none;\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 69.2pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"92\"\u003e\n\u003cp style=\"text-align: center; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 150%; font-family: verdana, geneva;\"\u003eName\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 362.95pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"484\"\u003e\n\u003cp style=\"text-align: center; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 150%; font-family: verdana, geneva;\"\u003eDescription\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 69.2pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"92\"\u003e\n\u003cp style=\"text-align: center; line-height: 150%; margin: 6.0pt 0in 6.0pt 0in;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cem\u003e\u003cspan style=\"line-height: 150%;\"\u003ea_heavy\u003c/span\u003e\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 362.95pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"484\"\u003e\n\u003cp style=\"line-height: 150%; margin: 6.0pt 0in 6.0pt 0in;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 150%; font-family: verdana, geneva;\"\u003eNumber of heavy atoms\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 69.2pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"92\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cem\u003e\u003cspan style=\"line-height: 150%;\"\u003ea_acc\u003c/span\u003e\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 362.95pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"484\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 150%; font-family: verdana, geneva;\"\u003eNumber of hydrogen bond acceptor atoms\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 69.2pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"92\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cem\u003e\u003cspan style=\"line-height: 150%;\"\u003ea_IC\u003c/span\u003e\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 362.95pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"484\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cspan style=\"line-height: 150%;\"\u003eAtom information content (total). Let \u003cem\u003en\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e be the number of occurrences of atomic number \u003cem\u003ei\u003c/em\u003e in the molecule, \u003cem\u003ep\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e\u0026nbsp;=\u0026nbsp;\u003cem\u003en\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e\u0026nbsp;/\u0026nbsp;\u003cem\u003en\u003c/em\u003e where \u003cem\u003en\u003c/em\u003e is the sum of the \u003cem\u003en\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e. Then \u003c/span\u003e\u003cem\u003e\u003cspan style=\"line-height: 150%;\"\u003ea_IC\u003c/span\u003e\u003c/em\u003e\u003cspan style=\"line-height: 150%;\"\u003e = -\u003cem\u003en\u003c/em\u003e\u0026sum;\u003cem\u003ep\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e\u003c/span\u003e\u003cspan style=\"line-height: 150%;\"\u003e\u0026nbsp;log\u0026nbsp;\u003cem\u003ep\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 69.2pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"92\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cem\u003e\u003cspan style=\"line-height: 150%;\"\u003eb_single\u003c/span\u003e\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 362.95pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"484\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 150%; font-family: verdana, geneva;\"\u003eNumber of single bonds (not including aromatic bonds).\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 69.2pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"92\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cem\u003e\u003cspan style=\"line-height: 150%;\"\u003ebpol\u003c/span\u003e\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 362.95pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"484\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cspan style=\"line-height: 150%;\"\u003eSum of the absolute value of the difference between atomic polarizabilities\u003c/span\u003e\u003cspan style=\"line-height: 150%;\"\u003e[\u003ca href=\"#_ENREF_55\"\u003e\u003cspan style=\"color: windowtext; text-decoration: none; text-underline: none;\"\u003e55\u003c/span\u003e\u003c/a\u003e]\u003c/span\u003e\u003cspan style=\"line-height: 150%;\"\u003e of all bonded atoms in the molecule \u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 69.2pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"92\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cem\u003e\u003cspan style=\"line-height: 150%;\"\u003echi0\u003c/span\u003e\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 362.95pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"484\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cspan style=\"line-height: 150%;\"\u003eAtomic connectivity index (order 0) \u003c/span\u003e\u003cspan style=\"line-height: 150%;\"\u003e[\u003ca href=\"#_ENREF_56\"\u003e\u003cspan style=\"color: windowtext; text-decoration: none; text-underline: none;\"\u003e56\u003c/span\u003e\u003c/a\u003e, \u003ca href=\"#_ENREF_57\"\u003e\u003cspan style=\"color: windowtext; text-decoration: none; text-underline: none;\"\u003e57\u003c/span\u003e\u003c/a\u003e]\u003c/span\u003e\u003cspan style=\"line-height: 150%;\"\u003e. The sum of 1/sqrt(\u003cem\u003ed\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e) over all heavy atoms \u003cem\u003ei\u003c/em\u003e with \u003cem\u003ed\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e\u0026nbsp;\u0026gt;\u0026nbsp;0, where \u003cem\u003ed\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e is the number of bonded neighbors of the atom \u003cem\u003ei\u003c/em\u003e in the hydrogen suppressed graph\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 69.2pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"92\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cem\u003e\u003cspan style=\"line-height: 150%;\"\u003eVDistMa\u003c/span\u003e\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 362.95pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"484\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 150%; font-family: verdana, geneva;\"\u003eSum of log\u003csub\u003e2\u003c/sub\u003e\u0026nbsp;\u003cem\u003em\u0026nbsp;-\u0026nbsp;D\u003csub\u003eij\u003c/sub\u003e\u003c/em\u003e\u0026nbsp;log\u003csub\u003e2\u003c/sub\u003e\u0026nbsp;\u003cem\u003eD\u003csub\u003eij\u003c/sub\u003e\u003c/em\u003e\u0026nbsp;/\u0026nbsp;\u003cem\u003em\u003c/em\u003e over all \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e, where \u003cem\u003eD\u003csub\u003eij\u003c/sub\u003e\u003c/em\u003e is the length of the shortest path from atoms \u003cem\u003ei\u003c/em\u003e to \u003cem\u003ej\u003c/em\u003e, and \u003cem\u003em\u003c/em\u003e is the sum of \u003cem\u003eD\u003csub\u003eij\u003c/sub\u003e\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 8.35pt;\"\u003e\n\u003ctd style=\"width: 69.2pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 8.35pt;\" width=\"92\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cem\u003e\u003cspan style=\"line-height: 150%;\"\u003eweinerPol\u003c/span\u003e\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 362.95pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 8.35pt;\" width=\"484\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cspan style=\"line-height: 150%;\"\u003eWiener polarity number: half the sum of all the distance matrix entries with a value of 3 as defined in \u003c/span\u003e\u003cspan style=\"line-height: 150%;\"\u003e[\u003ca href=\"#_ENREF_58\"\u003e\u003cspan style=\"color: windowtext; text-decoration: none; text-underline: none;\"\u003e58\u003c/span\u003e\u003c/a\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 69.2pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"92\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cem\u003e\u003cspan style=\"line-height: 150%;\"\u003eKier1\u003c/span\u003e\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 362.95pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"484\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cspan style=\"line-height: 150%;\"\u003eFirst kappa shape index: (\u003cem\u003en\u003c/em\u003e-1)\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;/\u003cem\u003e\u0026nbsp;m\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, where \u003cem\u003en \u003c/em\u003eand \u003cem\u003em \u003c/em\u003edenotes the number of atoms and the number of bonds in the hydrogen suppressed graph respectively\u003c/span\u003e\u003cspan style=\"line-height: 150%;\"\u003e[\u003ca href=\"#_ENREF_56\"\u003e\u003cspan style=\"color: windowtext; text-decoration: none; text-underline: none;\"\u003e56\u003c/span\u003e\u003c/a\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 69.2pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"92\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cem\u003e\u003cspan style=\"line-height: 150%;\"\u003ePEOE_VSA_PPOS\u003c/span\u003e\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 362.95pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"484\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cspan style=\"line-height: 150%;\"\u003eTotal positive polar van der Waals surface area. Sum of the \u003cem\u003ev\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e such that \u003cem\u003eq\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e is greater than 0.2. The \u003cem\u003ev\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e is calculated using a connection table approximation and \u003cem\u003eq\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e is the partial charge of atom \u003cem\u003ei\u003c/em\u003e calculated by the Partial Equalization of Orbital Electronegativities (PEOE) method\u003c/span\u003e\u003cspan style=\"line-height: 150%;\"\u003e[\u003ca href=\"#_ENREF_59\"\u003e\u003cspan style=\"color: windowtext; text-decoration: none; text-underline: none;\"\u003e59\u003c/span\u003e\u003c/a\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 69.2pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"92\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cem\u003e\u003cspan style=\"line-height: 150%;\"\u003evdw_area\u003c/span\u003e\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 362.95pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"484\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 150%; font-family: verdana, geneva;\"\u003eArea of van der Waals surface\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 69.2pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"92\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cem\u003e\u003cspan style=\"line-height: 150%;\"\u003emr\u003c/span\u003e\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 362.95pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"484\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 150%; font-family: verdana, geneva;\"\u003eMolecular refractivity\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 69.2pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"92\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cem\u003e\u003cspan style=\"line-height: 150%;\"\u003ezagreb\u003c/span\u003e\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 362.95pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"484\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 150%; font-family: verdana, geneva;\"\u003eZagreb index: the sum of \u003cem\u003ed\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e over all heavy atoms \u003cem\u003ei\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 69.2pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"92\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cem\u003e\u003cspan style=\"line-height: 150%;\"\u003eStd_dim3\u003c/span\u003e\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 362.95pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"484\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 150%; font-family: verdana, geneva;\"\u003eStandard dimension 3: the square root of the third largest eigenvalue of the covariance matrix of the atomic coordinates. A standard dimension is equivalent to the standard deviation along a principal component axis\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp style=\"margin-left: 21.3pt; text-indent: -21.3pt; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp style=\"line-height: 200%; text-align: left;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003eTable 2. The validation result of ten CYP predictive models by four methods, represented by AUC.\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"border-collapse: collapse; border: none;\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 13.5pt;\"\u003e\n\u003ctd style=\"width: 94.95pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"127\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e1A1\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e1A2\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2A6\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2B6\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36.85pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"49\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2C19\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2C8\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2C9\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2D6\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2E1\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e3A4\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13.5pt;\"\u003e\n\u003ctd style=\"width: 94.95pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"127\"\u003e\n\u003cp style=\"text-align: center; line-height: 150%; margin: 6.0pt 0in 6.0pt 0in;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cstrong\u003e\u003cspan style=\"line-height: 150%;\"\u003eImproved Bayesian method\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%; margin: 6.0pt 0in 6.0pt 0in;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.92\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%; margin: 6.0pt 0in 6.0pt 0in;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.84\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%; margin: 6.0pt 0in 6.0pt 0in;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.87\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%; margin: 6.0pt 0in 6.0pt 0in;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.88\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36.85pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"49\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%; margin: 6.0pt 0in 6.0pt 0in;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.84\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%; margin: 6.0pt 0in 6.0pt 0in;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.80\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%; margin: 6.0pt 0in 6.0pt 0in;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.83\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%; margin: 6.0pt 0in 6.0pt 0in;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.92\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%; margin: 6.0pt 0in 6.0pt 0in;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.91\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%; margin: 6.0pt 0in 6.0pt 0in;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.88\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13.5pt;\"\u003e\n\u003ctd style=\"width: 94.95pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"127\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cstrong\u003e\u003cspan style=\"line-height: 150%;\"\u003ena\u0026iuml;ve Bayesian method\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.79\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.70\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.81\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.76\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36.85pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"49\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.72\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.69\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.71\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.82\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.85\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; 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border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"49\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.75\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.73\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.77\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.84\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.88\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.81\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13.5pt;\"\u003e\n\u003ctd style=\"width: 94.95pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"127\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 150%;\"\u003e\u003cspan style=\"font-size: 10pt; font-family: verdana, geneva;\"\u003e\u003cstrong\u003e\u003cspan style=\"line-height: 150%;\"\u003eSVM\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.85\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.73\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.83\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.80\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36.85pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"49\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.75\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.76\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.79\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.88\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.89\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 33.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt; height: 13.5pt;\" width=\"44\"\u003e\n\u003cp style=\"margin-bottom: 6.0pt; text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.82\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp style=\"margin-left: 21.3pt; text-indent: -21.3pt; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp style=\"margin-left: 21.3pt; text-indent: -21.3pt; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003eTable 3. The results of independent test set calculated by the improved Bayesian method\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"border-collapse: collapse; border: none;\" width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 13.5pt;\"\u003e\n\u003ctd style=\"width: 15.9%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"15%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e1A1\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e1A2\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2A6\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2B6\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2C19\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2C8\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2C9\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2D6\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2E1\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e3A4\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003eAvg.\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13.5pt;\"\u003e\n\u003ctd style=\"width: 15.9%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"15%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003eSpecificity\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.95\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.93\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.95\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.94\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.87\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.91\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.90\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.86\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.95\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.72\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.90\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13.5pt;\"\u003e\n\u003ctd style=\"width: 15.9%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"15%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003eSensitivity\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.58\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.48\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.75\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.53\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.71\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.56\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.66\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.76\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.75\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.86\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.66\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13.5pt;\"\u003e\n\u003ctd style=\"width: 15.9%; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"15%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003eAccuracy\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.90\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.81\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.93\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.86\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.85\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.86\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.85\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.83\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.93\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.78\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 7.64%; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 2.85pt 0in 2.85pt; height: 13.5pt;\" width=\"7%\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e0.86\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003eTable 4. The number of substrates metabolized by each CYP450 isoform\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"width: 100.0%; border-collapse: collapse; border: none;\" width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: .2in;\"\u003e\n\u003ctd style=\"width: 9.12%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: .2in;\" width=\"9%\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e1A1\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 9.1%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: .2in;\" width=\"9%\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e1A2\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 9.1%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: .2in;\" width=\"9%\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2A6\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 9.1%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: .2in;\" width=\"9%\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2B6\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 9.1%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: .2in;\" width=\"9%\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2C19\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 9.1%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: .2in;\" width=\"9%\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2C8\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 9.1%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: .2in;\" width=\"9%\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva;\"\u003e2C9\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 9.1%; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; 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Each CYP isoform is able to metabolize a large number of compounds, and if patients take more than one drugs during the treatment, it is possible that some drugs would be metabolized by the same CYP isoform, leading to potential drug-drug interactions and side effects. Therefore, it is necessary to investigate the isoform specificity of CYP substrates. In this study, we constructed a data set consisting of 10 major CYP isoforms associated with 776 substrates, and used machine learning methods to construct the predictive models based on the features of structural and physicochemical properties of substrates. We also proposed a new method called Improved Bayesian method, which is suitable for small data sets and is able to construct more stable and accurate predictive models compared with other traditional machine learning models. Based on this method, the predictive performance of our method got the accuracy of 86% for the independent test, which was significantly better to the existing models. We believe that our proposed method will facilitate the understanding of drug metabolisms and help the large-scale analysis of drug-drug interactions.\u003c/p\u003e","manuscriptTitle":"Computational Prediction of the Isoform Specificity of Cytochrome P450 Substrates by an Improved Bayesian Method","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2019-05-23 19:34:55","doi":"10.21203/rs.2.9738/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"38143e50-3cad-4f1e-a5b9-32145d95299b","owner":[],"postedDate":"May 23rd, 2019","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":12440,"name":"Bioinformatics"},{"id":12441,"name":"Computational Biology"},{"id":12442,"name":"Drug Discovery, Design, \u0026 Development"}],"tags":[],"updatedAt":"","versionOfRecord":[],"versionCreatedAt":"2019-05-23 19:34:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-961","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"identity":"rs-961","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}