Exploration and practice of "disease—drug" potential association prediction based on "Swanson framework+Bioinformatics"—a case study of Autism Spectrum Disorder

Exploration and practice of "disease—drug" potential association prediction based on "Swanson framework+Bioinformatics"—a case study of Autism Spectrum Disorder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Exploration and practice of "disease—drug" potential association prediction based on "Swanson framework+Bioinformatics"—a case study of Autism Spectrum Disorder Yanhua Lv, Yuyang Yuan, Xiaoyun Zhong, Hongxia Zhao, Baoqiang Qu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4212015/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 3 You are reading this latest preprint version Abstract Objective Compared to traditional intermediate concepts, specific bioinformatics entities are more informative and higher directional. This study is based on the BITOLA system and combines bioinformatics methods to determine the intermediate concept which is key to improve efficiency of Literature-based Knowledge Discovery, proposes the concept of "Swanson framework + Bioinformatics", and conducts practice of Literature-based Knowledge Discovery to improve the scientificity and efficiency of research and development. Methods Firstly, detected the disease related genes (i.e. differentially expressed genes) according to the results of gene functional analysis as intermediate concepts to carry out Literature-based Knowledge Discovery. Taking the disease "Autism Spectrum Disorder(ASD)" as an example, the potential "disease-drug" association was predicted, and the predicted drugs were verified from the perspective of bioinformatics. Results Two drugs potentially associated with ASD were found: fish oil and forskolin, which were closely related to ASD in bioinformatics analysis results and literature verification.The two "disease-drug" association results showed better scientificity. The BIOINF-ABC + model improves the accuracy of calculations by 76% compared to using the BITOLA system alone.In addition, it also shows high accuracy and credibility in literature verification. Conclusion The BIOINF-ABC + model based on the "Swanson framework + Bioinformatics" has good practicality, applicability, and accuracy in conducting "disease-drug" association prediction in the biomedical field, and can be used for mining "disease-drug" relationships. Health sciences/Medical research Health sciences/Diseases/Psychiatric disorders/Autism spectrum disorders Biological sciences/Drug discovery Biological sciences/Drug discovery/Drug screening Biological sciences/Computational biology and bioinformatics Biological sciences/Computational biology and bioinformatics/Data mining Biological sciences/Computational biology and bioinformatics/Functional clustering Biological sciences/Computational biology and bioinformatics/Gene ontology Biological sciences/Computational biology and bioinformatics/Gene regulatory networks Biological sciences/Computational biology and bioinformatics/Genome informatics Biological sciences/Computational biology and bioinformatics/High throughput screening Biological sciences/Computational biology and bioinformatics/Literature mining Biological sciences/Computational biology and bioinformatics/Predictive medicine Literature-based Knowledge Discovery Autism Spectrum Disorders Differentially Expressed Genes Drug Discovery Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Data mining appeared in the late 1980s and was first developed in the field of databases, which is called Knowledge Discovery in Databases (KDD). [1] The concept of knowledge discovery was first formally proposed at the 11th International Joint Artificial Intelligence Conference held in the United States in 1989. Since then, knowledge discovery has begun to flourish. The so-called Literature-based Knowledge Discovery is a classical information science method that identifies effective, novel, potentially useful and ultimately understandable knowledge from the content of unrelated literature through literature mining to discover the cross domain knowledge transfer and implicit correlation. [2] This method was proposed by professor Swanson, a famous American information scientist, in 1985. It describes how to obtain the undiscovered implied association from two types of unrelated literature. The general idea is: if one published article reports the meaningful association between A and B, and the other reports the association between B and C, but there is no literature about the association between A and C, The new relationship between A and C can be obtained by considering the two literatures together. Professor Swanson developed a knowledge discovery tool based on the principle of this method and put forward two hypotheses. One is that eating fish oil may change some blood parameters to treat Raynaud's syndrome, [2] and the other is that magnesium deficiency can lead to migraine. [3] These two hypotheses were later verified by clinical experiments. [4,5] On this basis, many scientists continue to put forward new ideas. Gordon, a professor at the University of Michigan in the United States, and his collaborators successfully reproduced the scientific hypothesis of "the relationship between edible fish oil and Raynaud's disease" and "the relationship between magnesium and migraine", [6,7] and developed a set of methods for knowledge discovery based on computer retrieval. According to the model from source literature to intermediary literature and then to target literature, they were used to assist in knowledge discovery of unrelated literature. Weeber proposed a "two-step discovery model", which successfully reproduced the relationship between Raynaud's disease and fish oil, magnesium and migraine, and formally defined the two steps of the process of knowledge discovery as "open discovery" and "closed verification", that is, the process of open knowledge discovery is to find the intermediate word B through A, and then find C; The process of closed knowledge discovery is a process of testing hypotheses, starting from A and C to find a common intermediate concept B. Stegmann and Grohmann verified the process of Swanson's knowledge discovery by using co-occurrence word clustering analysis, [8] found eigenvalues based on the ratio of centripetality and density, and quickly determined the clustering of possible intermediate words and unrelated literature words. Hristovski et al. proposed a literature-based interactive biomedical discovery support system BITOLA, [9] which aims to discover the potential relationship between biomedical concepts (including MeSH (medical subject title) and human genes from HUGO) by mining MEDLINE database, so as to help biomedical researchers propose or verify new knowledge discoveries. In the above studies, Arrowsmith, a knowledge discovery tool, mainly selects intermediate concepts based on semantics and co-occurrence frequency. Gordon believes that the intermediary literature is best identified by absolute word frequency, and the target literature is best generated from the intermediary literature by using relative frequency. The BITOLA system developed by Hristovski mainly selects intermediate concepts based on MeSH vocabulary and its semantic types, while Johannes Stegmann and others mainly select intermediate concepts based on centripetality and density. It can be seen that in the Literature-based Knowledge Discovery, researchers have different methods of selecting intermediate concepts, and the purpose is to find a fulcrum to increase the accuracy of knowledge discovery. Although the intermediate concept mentioned above increases the diversity of entries for knowledge discovery, it also improves the accuracy of prediction. However, compared with a large number of concept groups, the prediction target is still large, and it is still not easy to quickly find intermediate concepts with higher accuracy. 2 Function and role of Bioinformatics Bioinformatics is a subject that studies the collection, processing, storage, dissemination, analysis and interpretation of biological information. It reveals the biological laws of a large number of complex biological data through the comprehensive use of biology, computer science and information technology. [10] Bioinformatics analysis is a method to explore biological related problems through the analysis of biological sequence, protein structure and literature data. [11] With the development of science and technology, traditional biological data (such as species basic data, physiological and biochemical data, trait genetics, environmental data, etc.) and various omics data (such as genome, transcriptome, proteome, metabolome, epigenome, phenotypic group, etc.) are accumulating, providing a data basis for knowledge discovery from the perspective of Bioinformatics. At the same time, massive data and complex background have led to the rapid development and application of machine learning, statistical data analysis and system description methods in Bioinformatics, [12,13] which can help researchers better understand gene expression profiles, realize gene function prediction, molecular structure relationship prediction, [14] and discover the hidden knowledge from massive biological data. Bioinformatics is often used in the biomedical field to study the hidden information of diseases or drugs in organisms. Hai et al. studied the predictive value of the molecular characteristics of drug target genes for the sensitivity of targeted drugs in gastric cancer, and found genes that can predict the prognosis of patients and the efficacy of targeted compounds. [15] Zhang et al. screened out the differentially expressed genes shared by important brain tissues related to heat stress based on geo database. [16] In this study, based on omics data, Bioinformatics analysis was used to calculate differentially expressed genes, go functional enrichment, KEGG pathway enrichment, etc. by using statistical methods in R language, in order to find the potential knowledge or association hidden in biological genes. 3 Exploration and practice of using Bioinformatics as an intermediate concept to carry out Literature-based Knowledge Discovery In the process of Literature-based Knowledge Discovery, the core is to determine the intermediate concept, and an accurate intermediate concept is the key to improve the efficiency of knowledge discovery. Although the text source field in the latest version of Arrowsmith system has been extended to the fields of document title, subject words and abstract, and the text processing time has been shortened, its natural language processing function is relatively limited, and the number of intermediate concept results provided is large, so it is unable to accurately and quickly identify the required biomedical concepts. On this basis, BITOLA system can accurately extract biomedical concepts by introducing MeSH vocabulary and natural language processing technology to support semantic prediction for the discovery of specific relationship types of "disease-gene". However, due to its wide variety and large number, it is still unable to accurately identify effective biomedical concepts. How to effectively reduce the noise of intermediate concept set has been the goal of researchers for many years. To solve this problem, other systems have also adopted measures. For example, BITOLA uses association rules instead of co-occurrence word frequency to express the relevance of concepts, DAD (Drug-Ad verse drug reactions-Disease) system uses concept frequency to sort intermediate concepts in the open discovery process, LitLinker uses UMLS semantic network to filter, and uses association rule mining algorithm to determine association concepts, but the fact is that despite this, It is still unable to effectively solve the problem of too many interfering words. Therefore, the efficiency of knowledge discovery cannot be truly solved only through these original unprocessed traditional intermediate concepts. On the basis of traditional methods, if entity information that is crucial, informative, and more directional for a certain disease or drug is used as an intermediate concept, it will undoubtedly be a highly filtered primary traditional intermediate concept, which will greatly improve the credibility and accuracy of knowledge discovery results. These more accurate and reliable entity information can be obtained through Bioinformatics analysis, that is, through the processing, analysis and mining of biomolecular data, the specific Bioinformatics entities in deep level can be extracted. Compared with the traditional intermediate concept, a specific Bioinformatics entity covers more information and has higher directivity. If it is used as an intermediate concept to carry out Literature-based Knowledge Discovery, it will greatly improve the scientificity and efficiency of research and development, such as detecting disease-related genes (i.e., differentially expressed genes) according to the results of gene function analysis. Therefore, based on the BITOLA system, this study combined with Bioinformatics methods to determine the intermediate concept, put forward the knowledge discovery concept of "Swanson framework+Bioinformatics", and carried out the exploration and practice of knowledge discovery in unrelated literature, in order to improve the prediction efficiency(the technical roadmap is shown in Figure 1). 3.1 Proposing "Swanson framework+Bioinformatics" knowledge discovery (referred to as "BIOINF-ABC + ") Knowledge discovery based on "Swanson framework+Bioinformatics", that is, Literature-based Knowledge Discovery based on the intermediate concept of Bioinformatics, refers to the use of important deep-seated information about organisms (such as differentially expressed genes) obtained from Bioinformatics analysis as the intermediate concept of ABC model to explore the potential "disease-drug" relationship, referred to as "BIOINF-ABC + ". This study selected the disease Autism Spectrum Disorder (hereinafter referred to as ASD or auitsm) to explore the practice of knowledge discovery in the unrelated literature, in order to evaluate the feasibility of the concept and the accuracy of the prediction results. 3.1.1 BIOINF-ABC + result sorting algorithm The algorithm follows the knowledge discovery algorithm of BITOLA system, that is, based on the association rules representing the known relationships between concepts and considering the background knowledge, a new relationship between concepts is proposed. In order to check the results as easily as possible, the related concepts are sorted. Related concepts Y can be sorted by association rule support (co-occurrence frequency), confidence or semantic type. The related concepts Z can be sorted by the following calculation formula: The ranking is calculated based on support, but it can also be calculated based on confidence. In this equation, Z k is the concept of calculating its rank, S XYi and S YiZk are the support of association rule X → Y i and Y i → Z k , and m is the number of intermediate concepts Y. 3.1.2 Calculation of differentially expressed genes Bioinformatics analysis results include differentially expressed genes, GO functional enrichment, KEGG pathway enrichment, etc. Among them, differential expressed genes (DEGs) refer to genes with significant differences in RNA expression due to environment, time and other factors. For drug research, differential expressed genes analysis is very important, such as network pharmacology, an emerging discipline based on high-throughput omics data analysis, computer virtual computing and network database retrieval 17 , which can enable researchers to recognize the mechanism of drug action from a new perspective (differentially expressed genes); Yang et al. used high-throughput omics data to calculate differentially expressed genes, etc., applied biological big data to rapid rational drug design, inspiring and accelerating the discovery process of existing antifungal drugs; [18] Du screened potential biomarkers of Sjogren's syndrome by calculating differentially expressed genes and Hub genes, providing new insights into the pathogenesis of Sjogren's syndrome. [19] Differentially expressed genes are the basis of Bioinformatics analysis and drug research. Researchers can analyze the potential information of diseases and drugs, such as targets and biomarkers, through differentially expressed genes, providing researchers with new insights and new research directions. Therefore, differentially expressed genes are a key and necessary element in the research of “disease-drug” potential association. Taking them as intermediate concepts is an important basis for improving the scientificity and accuracy of knowledge discovery research. Therefore, this paper takes one of the results of Bioinformatics analysis, differentially expressed genes, as an example, and takes them as intermediate concepts to explore the practical effect of the new method "BIOINF-ABC + " for Literature-based Knowledge Discovery. The calculation method of differentially expressed genes in this study is FC (fold change) algorithm. The principle of the algorithm is to calculate the multiple of the average expression level of genes in the two types of samples. If the value reaches the preset threshold (generally set to 2, which is greater than 1 or less than -1 in the logarithmic expression ratio based on 2), the gene is judged to be differentially expressed. The calculation formula is as follows: FC represents the calculation method of differentially expressed genes; is the average expression value of gene i in X samples; is the average expression value of gene i in Y samples. 3.1.3 Determination and analysis steps of literature collection For the construction of the initial concept set, this study uses the BITOLA system strategy, which extracts the concepts in the title, abstract and MeSH fields of PubMed related literature as the initial concepts. For intermediate concept sets, the large number of concept sets will cause great interference to the discovery of truly meaningful target concepts. BIOINF-ABC + knowledge discovery model in order to improve the quality of target concepts, the intermediate concept set is filtered by Bioinformatics methods. After determining the target disease or drug, this method needs to select the appropriate data in the gene expression database to achieve Bioinformatics analysis and obtain a specific intermediate concept set. Choose one of the differential genes, pathways or proteins in the intermediate concept set as the intermediate concept (Y) of this study. At the same time, on the basis of the target concept set, the results are still screened by combining Bioinformatics methods (such as protein interaction network and pathway analysis). Of course, different Bioinformatics analysis methods (such as differentially expressed genes, pathways, proteins or immune infiltrating cells) may be used for different intermediate concepts or target concepts, which greatly improves the efficiency of target concept hit. 3.2 Practice of "BIOINF-ABC + " Literature-based Knowledge Discovery: taking the discovery of potential relationship of “ASD-drugs" as an example 3.2.1 Differentially expressed genes calculation of ASD The GEO(Gene Expression Omnibus) database was selected as the data source to obtain the experimental genes of ASD. The R language limma program package was used to calculate the differentially expressed genes, [20] and the intersection genes with opposite regulatory effects in the differentially expressed genes were removed. The screening conditions were :|log2 (Fold Change) |>0.5, P<0.05. 105 genes with significant differential expression of ASD were obtained, including 57 up-regulated and 48 down-regulated genes; The clusterprofiler package was used to analyze the KEGG pathway enrichment of significantly differentially expressed genes, [21] and 60 pathways enriched by up-regulated genes and 79 pathways enriched by down-regulated genes were obtained. 3.2.2 Take the concept of Bioinformatics (differentially expressed gene) as an intermediate concept to carry out knowledge discovery First, take Autistic Disorder as the initial concept input, and look for the intermediate concept Y related to X (semantic type is "Gene or Gene Product"). The search results showed that there were 340 genes related to the Autistic Disorder, which intersected with the previously calculated ASD DEGs list. The differentially expressed genes obtained were IFI6,LPL,BRWD2, which were the selected Y. Then find the relevant Z according to Y (semantic type is "Organic Chemical/Pharmaceutical Substance"), and the result is the list of Z drugs potentially associated with X disease. According to the calculation results, 594 drugs were found, and this study selected the top 50 drugs with the highest semantic frequency for subsequent research (see Table 1). Table 1. Results of BITOLA knowledge discovery system (top 50 drugs) Serial number Drug name Frequency Score Serial number Drug name Frequency Score 1 Heparin 687 50 26 Triiodothyronine 39 25 2 Recombinant Insulin 624 49 27 Tetrachlorodibenzodioxin 36 24 3 Enzyme Inhibitors 153 48 28 Methionine 36 23 4 Interferon Type II 138 47 29 Fish Oils 36 22 5 Antilipemic Agents 102 46 30 Fatty Acids, Omega-3 33 21 6 Recombinant Cytokines 84 45 31 orlistat 30 20 7 Dexamethasone 81 44 32 Recombinant Interferon-gamma 27 19 8 Serine 81 43 33 Fenofibrate 27 18 9 Somatotropin 78 42 34 Bucladesine 27 17 10 Hypoglycemic Agents 69 41 35 Anticholesteremic Agents 27 16 11 Recombinant Interleukin-1 66 40 36 Arginine 27 15 12 Fat Emulsions, Intravenous 66 39 37 Amino Acids 27 14 13 Recombinant Interferon 66 38 38 Somatropin 24 13 14 Thiazolidinediones 60 37 39 rosiglitazone 24 12 15 Interferon Type I 60 36 40 glucagon (rDNA) 24 11 16 Glycerol 60 35 41 Recombinant Interleukin-4 24 10 17 Recombinant Interleukin-2 60 34 42 Sodium Chloride 24 9 18 Tetradecanoylphorbol Acetate 57 33 43 Tretinoin 24 8 19 Lecithin 54 32 44 Antineoplastic Agents 24 7 20 Weight Loss 54 31 45 Heparin, Low-Molecular-Weight 24 6 21 mitochondrial uncoupling protein 48 30 46 Immune Sera 24 5 22 4-diethoxyphosphorylmethyl-N-(4-bromo-2-cyanophenyl)benzamide 48 29 47 Polyethylene Glycols 24 4 23 Interferon-alpha 42 28 48 Protamines 24 3 24 Oleic Acid 42 27 49 Adenosine Triphosphate 24 2 25 Isoproterenol 39 26 50 Forskolin 24 1 The method of Bioinformatics was used to find the "disease-drug" correlation in the results. Select the top 50 drugs for analysis and screening: exclude the results of drugs belonging to class I and drugs without experimental data in the GEO database, and finally get 16 drugs (see Table 2). Bioinformatics analysis of these drugs was carried out and compared with the Bioinformatics analysis results of autism. Using GEO database as the data source, the experimental genes of 16 drugs were obtained. The R language limma program package was used to calculate the differentially expressed genes, [20] and excel was used to remove the intersection genes with opposite regulatory effects from the calculated differentially expressed genes, and the R language program was used to average the expression of differentially expressed genes with the same regulatory effect; The clusterprofiler package was used to enrich the KEGG pathway of differentially expressed genes. [21] By comparing the DEGs with opposite expression of ASD and their enriched KEGG pathway, we found that the drugs with closer association with ASD, and the specific results are shown in Table 2. Table 2. Comparison results of drugs and ASD (results showing opposite expression of drugs and ASD) Drug Name Number of experiments Differentially expressed genes KEGG pathway (Autism up-Drug down) KEGG pathway (Autism down-Drug up) Dexamethasone 13 6 60 79 Glycerol 4 10 58 79 Interferon-alpha 2 12 59 78 Oleic Acid 2 20 60 79 Triiodothyronine 1 32 60 79 Methionine 5 15 60 79 Fish Oils 12 26 59 79 Recombinant Interferon-gamma 1 42 60 79 Fenofibrate 16 7 60 79 Bucladesine 1 28 59 78 Arginine 2 16 59 78 Somatropin 1 26 59 77 Rosiglitazone 9 7 59 79 Glucagon 1 43 59 78 Tretinoin 8 9 60 78 Forskolin 9 26 60 79 Note: Number of experiments: differentially expressed genes were calculated for a control experiment consisting of 3 or more groups. The number of experiments represented the number of control experiments. For the above 16 drugs, after comprehensive consideration of the number of experiments in the data set, the complexity of data processing, the number of differentially expressed genes and KEGG pathways, it was found that (1) Although triiodothyronine,recombinant interferon γ,glucagon, growth hormone and bradysin are dominant in differentially expressed genes, they are not considered due to the small number of experiments; (2) Because the number of differentially expressed genes overlapped with ASD is too small, dexamethasone, glycerol, interferonα,oleic acid, methionine, fenofibrate, arginine, rosiglitazone, retinoic acid and other drugs will not be considered; (3) Both fish oil and forskolin have absolute advantages in terms of the number of experiments, differentially expressed genes, pathways and so on. Therefore, based on the Literature-based Knowledge Discovery results of "BIOINF-ABC + ", this study believes that fish oil and forskolin have high potential "drug-disease" association credibility for ASD. 3.3 Bioinformatics reverse verification for results of "BIOINF-ABC + " Literature-based Knowledge Discovery On the basis of the above research, this study analyzed the two drugs and ASD respectively by Bioinformatics method, verified the above analysis results from the Bioinformatics level, and made a deeper comparison and analysis of the two drugs. The first is to construct the Protein-Protein Interaction network of significantly different genes and calculate the key genes. Previously, the opposite part of the fish oil/forskolin differentially expressed genes has been removed and screened under the condition of |log2(Fold Change)|>0.5. 1129 significant differentially expressed genes in fish oil were obtained, including 529 up-regulated genes and 600 down-regulated genes; There were 1164 significant differentially expressed genes for forskolin, including 715 up-regulated genes and 449 down-regulated genes. Upload significant differentially expressed genes to STRING v11.0(https://string-db.org/), an online analysis website, to conduct Protein-Protein Interaction (PPI) network analysis, and take the confidence>0.4 as the threshold for screening. [22] Key genes are highly correlated genes in PPI network. In this study, the key genes are the top 10 genes with the highest frequency appear in the PPI network relationship. The CytoHubba plug-in 23 of the Cytoscape software 24 will rank proteins according to their properties in the network, and provide 12 topological analysis methods, such as Degree, Edge Percolated Component (EPC), Maximum Neighborhood Component (MNC), and score and rank proteins according to the corresponding algorithms. In this study, the CytoHubba plug-in of Cytoscape software was used to analyze the results of PPI network. The top 10 proteins of 12 algorithms were output, and the top 10 proteins of frequency were counted as core genes. The second is enrichment analysis. Previously, the R language clusterprofiler package has been used to enrich the KEGG pathway of differentially expressed genes. [21] Here, the R language is used to visualize the clustering results. Comparative analysis of the significant differentially expressed genes and KEGG pathway with the opposite regulatory effect of ASD and fish oil/forskolin showed that in terms of the differentially expressed genes, [22] 12 topological analysis methods such as Degree, EPC, MNC, DMNC and MCC of the CytoHubba plug-in in Cytoscape software were used to analyze the PPI network, and the frequency statistics of the top 10 proteins in each output score of the 12 algorithms were performed. The top 10 proteins were identified as core genes with important regulatory roles (see Figure 2). The R language clusterprofiler package was used to enrich the KEGG pathway of the significant differentially expressed genes between fish oil and forskolin. The results showed that fish oil had 285 pathways enriched by up-regulated genes and 294 pathways enriched by down-regulated genes. Screening was performed with a threshold of P<0.05, with 34 pathways enriched by up-regulated genes and 22 pathways enriched by down-regulated genes (see Figure 3 for some pathways). Additionally, forskolin had 300 pathways enriched by up-regulated genes and 236 pathways enriched by down-regulated genes, screened with a threshold of P<0.05. There are 45 pathways enriched by up-regulated genes and 10 pathways enriched by down-regulated genes (some pathways are shown in Figure 3). Among the pathways enriched by genes with significant differences in fish oil (P<0.05), there were three pathways containing both up-regulated and down-regulated genes: Neuroactive ligand-receptor interaction, Purine metabolism, and Biosynthesis of unsaturated fatty acids; The up-regulated genes enrichment pathways are mainly involved in cAMP signaling pathway, Ras signaling pathway, Cell adhesion molecules and other related functions or processes; Down-regulated genes enrichment pathways are mainly involved in p53 signaling pathway, Fatty acid metabolism, Cell cycle and other related functions or processes. Among the pathways enriched by genes with significant differences in forskolin (P<0.05), two pathways contain both up-regulated genes and down-regulated genes: Transcriptional misregulation in cancer and MAPK signaling pathway; The pathways of up-regulated genes enrichment are mainly involved in PPAR signaling pathway, p53 signaling pathway, IL-17 signaling pathway and other related functions or processes; Down -regulated genes enrichment pathways are mainly involved in Biosynthesis of amino acid, Nucleotide metabolism, Glycine, serine and threonine metabolism and other related functions or processes. It can be seen that among the significant differentially expressed genes screened by ASD and fish oil, there are four identical genes, including two genes with opposite regulatory effects: PTPRR and RASD1. The protein encoded by PTPRR gene is a member of Protein Tyrosine Phosphatase (PTP) family. PTP is a signal molecule that regulates various cellular processes, including cell growth, differentiation, mitotic cycle and carcinogenic transformation. RASD1 gene encodes a member of the small gtpase Ras superfamily. The coding protein is an activator of G protein signal transduction and serves as a direct nucleotide exchange factor of Gi-Go protein. Among the significant differentially expressed genes screened by ASD and forskolin, there were 8 identical genes, and 2 genes with opposite regulatory effects: RASD1 and DUSP14. Bispecific phosphatase DUSP is characterized by its ability to dephosphorylate tyrosine and serine/threonine residues. They are considered to be the main regulators of key signaling pathways. Among the KEGG pathways enriched by the significant differentially expressed genes screened from ASD and fish oil, 126 pathways were enriched by genes with opposite regulatory effects, among which the pathway satisfying P<0.05 in ASD and fish oil was 0, and the pathway satisfying P<0.1 was 1: Ovarian steroidogenesis. This pathway contains two ASD genes: PLA2G4B and FSHB; There are four fish oil genes: ACOT1, ACOT2, ADCY4 and ACOT4. These genes are mainly involved in Reproductive organ development, Fatty acid metabolism and other processes. Ovarian steroidogenesis: ovarian steroids, 17-βestradiol (E2) and progesterone (P4) are essential for normal uterine function, the establishment and maintenance of pregnancy and the development of mammary gland. The above six genes are shown in the ovarian steroidogenesis pathway map, which shows that these genes are mainly involved in GnRH signaling pathway. Among the KEGG pathways enriched by the significant differentially expressed genes screened by ASD and forskolin, 129 pathways were enriched by genes with opposite regulatory effects, among which one pathway satisfying P<0.05 in ASD and forskolin simultaneously: MAPK signaling pathway. This pathway contains three ASD genes: GADD45G, PTPRR and CSF1R; There are 11 forskolin genes: FLT3LG, RRAS2, RPS6KA2, EPHA2, CACNB4, ATF4, CSF1, FLNC, ANGPT2, GADD45A and DDIT3. These genes are mainly involved in cellular processes and inflammatory reactions. MAPK signaling pathway: mitogen activated protein kinase (MAPK) cascade is a highly conserved module involved in various cell functions, including cell proliferation, differentiation and migration. The above 14 genes are represented in the MAPK signaling pathway map, which shows that these genes are mainly involved in the classical MAP kinase pathway. The KEGG pathway of Ovarian steroidogenesis and MAPK signaling pathway are shown in Figure 4. In summary, 10 key genes were selected from the differentially expressed genes as the core of subsequent text verification. The results of pathway enrichment analysis showed that fish oil was involved in a key pathway of autism, namely Ovarian steroidogenesis pathway, which was involved in GnRH signal transduction; Forskolin is also involved in a key pathway of autism, namely MAPK signaling pathway, and its classical MAP kinase pathway. Therefore, the results of knowledge discovery based on "BIOINF-ABC + " have achieved good verification results in the level of Bioinformatics analysis. 3.4 T ext verification of results of "BIOINF-ABC + " Literature-based Knowledge Discovery In this study, the domain knowledge score method was used to verify the effectiveness of fish oil/forskolin targets in Chinese and English databases. The specific operation is as follows: take 10 key genes as key targets, search in the English database PubMed with "autism" and "key targets" as the key words, and search in the Chinese database CNKI and Wanfang database with "autism" and "key targets" as the key words, record the relevant search results and calculate their cumulative scores. Table 3 shows the retrieval results of the above key targets that are mainly involved in inflammatory response, cell cycle progression and other related processes in ASD patients. The results showed that fish oil and forskolin were highly correlated with ASD, especially forskolin. Tricholaryngin is a direct AC/cAMP/CREB activator, which is isolated from Angelica dahurica and has various neuroprotective properties. A number of studies have shown that the application of forskolin in the treatment of ASD is feasible. Alharbi et al. have shown that forskolin has been proved in their laboratory that it can directly activate adenylate cyclase (AC) and reverse neurodegeneration related to the progression of autism, multiple sclerosis, ALS and Huntington's disease. [25] Mehan et al. have shown that forskolin can alleviate neuronal mitochondrial dysfunction and improve neurological symptoms in autism rats. [26] Chi have shown that the agonist forskolin may regulate FMR1 gene mainly through the cAMP signaling pathway through the overlapping sites in the promoter region of FMR1, the pathogenic gene of fragile X syndrome. [27] Table 3. List of text verification results for 10 key targets based on domain knowledge scores Fish oil Forskolin Target name Correlation effect Domain knowledge score Target name Correlation effect Domain knowledge score CCNA2 Regulator of the cell cycle 1 CCNA2 Regulator of the cell cycle 1 CCNB1 Involved in controlling the G2 / M transition phase of the cell cycle 0 CCNB1 Involved in controlling the G2 / M transition phase of the cell cycle 0 IL6 Associated with a variety of inflammatory - related disease states 243+49+16 CXCL8 A major mediator of the inflammatory response 9+1+0 ESR1 Regulate the transcription of many estrogen induced genes 16+0+0 VEGFA Induce endothelial cell proliferation, promote cell migration and inhibit cell apoptosis 14+1+0 IL1B An important agent of inflammatory response and is involved in a variety of cellular activities 15+1 FOS Regulator of cell proliferation, differentiation and transformation. Associated with apoptotic cell death. 109+12+1 IL10 Play a pleiotropic role in immune regulation and inflammation 98+20+11 HDAC1 Involved in cell proliferation and differentiation, cell growth and apoptosis 10+2+7 BRCA1 Maintain genomic stability and act as a tumor suppressor 4 IGF1 A member of a family of proteins involved in mediating growth and development 74+4+3 CCND1 Involved in cell cycle processes 3 CYCS Involved in the initiation of apoptosis 0 BUB1 Play a central role in mitosis 3 CEBPB Play an important role in regulating genes involved in immune and inflammatory responses, among other processes 1 MKI67 Associated with cell proliferation 0 PPARGC1A Regulate the genes involved in energy metabolism 3+0+0 NTRK2(TRKB ) Send signals to the cell that lead to differentiation 9 PTGS2 Responsible for prostaglandin biosynthesis involved in inflammation and mitosis 23+1+0 PLK1 Involved in mitosis and apoptosis 2 TOP2A Involved in processes such as chromosome condensation, chromatid separation, and relief of torsional stress during DNA transcription and replication 2 In addition, mitochondrial dysfunction is one of the important pathological markers of autism, and mitochondrial dysfunction in autism is associated with decreased ATP levels due to decreased levels of cyclic adenylate monophosphate. The diterpenoid tricholaryngins extracted from tricholaryngins can regulate various physiological functions of cells by increasing cyclic adenylate monophosphate and up-regulating adenylate cyclase. In order to study the neuroprotective effect of tricholaryngine on autism, Mehan et al. (2020) from ISF School of Pharmacy, India, administered adenylate cyclase activator tricholaryngine intragastally to model rats with autism for 15 days at doses of 10, 20 and 30mg/kg. It was found that tricholaryngine can dose-dependently improve neuronal mitochondrial dysfunction, one of the important pathological markers of autism, and reduce the levels of pro-inflammatory cytokines, oxidative stress, and lipid biomarkers, further demonstrating the potential of adenylate cyclase activators in the treatment of autism (Chi, 2012). Fish oil supplementation may improve hyperactivity, lethargy, and stereotyping in people with autism, but the available clinical data is too limited to draw definitive conclusions.The above scientific research results once again demonstrate the potential therapeutic effects of these two drugs on autism. It can be seen that from the perspective of experimental evidence recorded in the literature, the results of knowledge discovery based on "BIOINF-ABC + " have also been well verified in the literature set, which proves that the scientific hypothesis (i.e. disease X-drug Z relationship) obtained by this method has a good experimental basis, so the possibility, feasibility and reliability of using this method to predict the potential drug disease relationship are high. 4 Discussion 4.1 Result analysis of "BIOINF-ABC + " Literature-based Knowledge Discovery This study used the BIOINF-ABC + model to carry out the Literature-based Knowledge Discovery, and predicted the association between forskolin and fish oil in the field of ASD, namely "forskolin-ASD" and "fish oil-ASD". The two groups of "drug-disease" association results showed good scientificity in the Bioinformatics analysis results, and also showed high accuracy and reliability in the text verification. From the above analysis results, BIOINF-ABC + model has good practicability, applicability and accuracy in the biomedical field of "drug-disease" association prediction. Therefore, it is feasible and efficient to use the results of Bioinformatics analysis as an intermediate concept for knowledge discovery on the basis of Literature-based Knowledge Discovery. Secondly, the BIOINF-ABC + model proposed in this study is scientific and can be used for knowledge discovery. At the same time, it also provides a new research idea for the future study of “drug-disease” relationship. 4.2 Efficiency analysis of "BIOINF-ABC + " Literature-based Knowledge Discovery Among the top 50 drugs retrieved by BITOLA system, fish oil ranked 29th and forskolin ranked 50th. Assuming that the full score of 50 points is given to each drug according to the order of drug occurrence, the drug ranking first is 50 points, and the drug ranking 50 is 1 point, including 22 points for fish oil and 1 point for Forskolin. At the same time, Bioinformatics analysis of these 50 drugs showed that fish oil and forskolin ranked the top 2 in the analysis results, and the results of BIOINF-ABC + model were also scored, with 50 points for the first drug and 49 points for the second drug, 50 points for fish oil and 49 points for forskolin (see Table 1 ). If the drug prediction accuracy is: $$\text{Y}=\frac{{\text{x}}_{\text{a}}+{\text{x}}_{\text{b}}}{50\times 2}$$ 3 Then, the accuracy of BITOLA system for calculating fish oil and Forskolin is Y BITOLA = \(\frac{22+1}{50\times 2}\) =23%; The accuracy of BIOINF-ABC + model in this study was Y BIOINF−ABC + = \(\frac{50+49}{50\times 2}\) =99%ཡ It can be seen from the comparison of the accuracy of prediction that the accuracy of BIOINF-ABC + model is 76% higher than that calculated by BITOLA system alone. Therefore, the BIOINF-ABC + model proposed in this study has high accuracy. 4.3 Exploration of "BIOINF-ABC + " Literature-based Knowledge Discovery application fields The BIOINF-ABC + model in this study is based on the BITOLA system. It is based on Medline database and retrieved by using the results of Bioinformatics analysis as an intermediate concept. It has achieved good results in the “disease-gene-drug” association. Therefore, the model has good applicability in the biomedical field. 5 Conclusion In conclusion, based on Swanson's Literature-based Knowledge Discovery and Bioinformatics, this study proposed the BIOINF-ABC + model. This study found that fish oil and forskolin had a certain therapeutic effect on ASD, which verified the scientificity and accuracy of the BIOINF-ABC + model, and provided new research ideas and research directions for future drug research. Declarations Acknowledgements We would like to express our deep appreciation to the participants who responded to this research.We also would like to express our deep appreciation to all the databases and researchers who provided the research data. Authors contributions L.Y.H. analyzed the data, wrote the paper, and revised the paper. Y.Y.Y. analyzed the data, and wrote the paper. Z.X.Y. analyzed the data.Z.H.X. organized original data. Q.B.Q. analyzed the data. L.X.C. provided methods guidance. Y.Q. provided strategic design and methods guidance.All authors read and approved the final manuscript. Data availability statement The data used in this study were all from publicly available databases.The bioinformatics data used in this study were sourced from the GEO database, with datasets consisting of GSE59927, GSE45577, GSE43723, GSE52684, GSE58062, GSE28482, GSE62673, GSE73195, GSE70922, GSE50945, GSE48368, GSE56166,GSE46914, GSE59927, GSE5258, GSE137033, GSE124935, GSE59927, GSE68144, GSE59927, GSE83891, GSE73385, GSE68266, GSE42438, GSE22631. Funding This study was supported by the National Social Science Foundation of China(Nos.20BTQ064). Conflict of interset declaration We have no conflict of interest in this article. References Wang, G. H., & Jiang, P. Data mining review. Journal of Tongji University (Natural Science Edition). 32(2) :246-252. https://doi.org/10.3321/j.issn:0253-374X.2004.02.023(2004) Swanson D. R. Fish oil, Raynaud's syndrome, and undiscovered public knowledge. Perspect Biol Med . 30(1) :7-18. https://doi.org/10.1353/pbm.1986.0087(1986) Swanson D. R. Migraine and magnesium: eleven neglected connections. Perspect Biol Med . 31(4) :526-57. https://doi.org/10.1353/pbm.1988.0009(1988) Swanson D. R. "Two medical literatures that are logically but not bibliographically connected." Journal of the Association for Information Science and Technology . 38(4) :228–233.(2010) Swanson D. R. A second example of mutually isolated medical literatures related by implicit, unnoticed connections. J Am Soc Inf Sci . 40(6) :432-435.(1989) Gordon M. D., Lindsay R.K. Toward discovery support systems: a replication, re-examination, and extension of swanson's work on literature-based discovery of a connection between raynaud's and fish oil. Journal of the Association for Information Science and Technology . 47(2) :116-128.(1996) Lindsay R. K, Gordon M. D. Literature-based discovery by lexical statistics. Journal of the American Society for Information Science . 50(7) :574-587.(1999) Stegmann J., Grohmann G. Hypothesis generation guided by co-word clustering. Scientometrics . 56(1) :111-135.(2003) Hristovski, D., Peterlin, B., Mitchell, J. A., & Humphrey, S. M. Using literature-based discovery to identify disease candidate genes. Int J Med Inform . 74(2-4) :289-298. https://doi.org/10.1016/j.ijmedinf.2004.04.024(2005) Hodgman TC. Bioinformatics.Chen M, Bao J. L., Huang B. D.Beijing: Science Press . 1-239. Fang Y. Application of data mining in bioinformatics. Microcomputer Development. 14(4) :1-3, 17. https://doi.org/10.3969/j.issn.1673-629X.2004.04.001(2004) Du W. Application of machine learning and data mining in bioinformatics. Jilin University (2011). Wen Z. et al. Comprehensive Genetic Analysis of Tuberculosis and Identification of Candidate Biomarkers. Front Genet . 13 :832739.https://doi.org/10.3389/fgene.2022.832739(2022) Zeng M. et al. Exploring drug usage patterns and pharmacological mechanisms of diabetes treatment based on data mining and bioinformatics. 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Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res . 2003;13(11):2498-504.https://doi.org/10.1101/gr.1239303 Chin C.H. et al. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol . 8 Suppl 4(Suppl 4):S11.https://doi.org/10.1186/1752-0509-8-S4-S11(2014) Alharbi M. et al. Effect of Natural Adenylcyclase/cAMP/CREB Signalling Activator Forskolin against Intra-Striatal 6-OHDA-Lesioned Parkinson's Rats: Preventing Mitochondrial, Motor and Histopathological Defects. Molecules . 27(22) :7951.https://doi.org/10.3390/molecules27227951(2022) Mehan S. et al. Adenylate cyclase activator forskolin alleviates intracerebroventricular propionic acid-induced mitochondrial dysfunction of autistic rats. Neural Regen Res . 15(6) :1140-1149.https://doi.org/10.4103/1673-5374.270316(2020) Chi X. F. Mechanism research: Re-expression of Fragile X Mental Retardation 1 gene induced by adenylate cyclase activator (Master's Thesis). Guangdong: Southern Medical University .(2012) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editor invited by journal 25 Apr, 2024 Submission checks completed at journal 25 Apr, 2024 First submitted to journal 03 Apr, 2024 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. We do this by developing innovative software and high quality services for the global research community. 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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-4212015","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":296895470,"identity":"260876cb-5fa9-444c-b5b4-b82627113851","order_by":0,"name":"Yanhua Lv","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACPhDB2MDAwMbMfODAhx9EaGGDaeFnZ0s8OLOHFC2S/TzGhznYiNEikWP4uXDHYXmDwzwfDjPwMMjzix0gqMVYeuaZw4YbDvNuOFxgwWA4c3YCAS08ZwykedsOM4K1zOBhSDC4TViL8W+gFvsNh3keHOZhI0YLe48ZyJbEmc08DMRqaSuz5m1LT+5nZjMABrIEYb/wMzNvvs3bZm3bxn/48YcPP2zk+aUJaGFg4DBA5kkQUg4C7A+IUTUKRsEoGAUjGQAA5/JBLTYMutAAAAAASUVORK5CYII=","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yanhua","middleName":"","lastName":"Lv","suffix":""},{"id":296895471,"identity":"d5149e38-cc65-4e3d-bf2c-b54409407805","order_by":1,"name":"Yuyang Yuan","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuyang","middleName":"","lastName":"Yuan","suffix":""},{"id":296895472,"identity":"b630e900-f4f8-40c5-845e-0f45772a5dc4","order_by":2,"name":"Xiaoyun Zhong","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyun","middleName":"","lastName":"Zhong","suffix":""},{"id":296895473,"identity":"f66b66ed-32a0-4e15-b404-0f2cf07fcec6","order_by":3,"name":"Hongxia Zhao","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongxia","middleName":"","lastName":"Zhao","suffix":""},{"id":296895474,"identity":"4e88abd0-c544-4529-81f0-afeca893db02","order_by":4,"name":"Baoqiang Qu","email":"","orcid":"","institution":"Institute of Scientific and Technical Information of China","correspondingAuthor":false,"prefix":"","firstName":"Baoqiang","middleName":"","lastName":"Qu","suffix":""},{"id":296895475,"identity":"f568aefa-a67e-43bc-83b2-2c3dbb276a77","order_by":5,"name":"Xuechun Lu","email":"","orcid":"","institution":"Second Medical Center of the Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xuechun","middleName":"","lastName":"Lu","suffix":""},{"id":296895476,"identity":"e5d6204a-18da-4cc2-893e-6278626b5c5a","order_by":6,"name":"Qi Yu","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2024-04-03 10:31:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4212015/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4212015/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-79587-6","type":"published","date":"2024-11-28T15:57:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55760736,"identity":"b1f27f52-fb12-4a7b-8385-02e5fb66a4c6","added_by":"auto","created_at":"2024-05-02 18:56:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54035,"visible":true,"origin":"","legend":"\u003cp\u003eTechnical roadmap of the \"BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e\" Literature-based Discovery Model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4212015/v1/875c7423ab700f9934894a91.png"},{"id":55762007,"identity":"a33fccab-3e8a-47eb-bcef-3b19861b643a","added_by":"auto","created_at":"2024-05-02 19:04:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":18007,"visible":true,"origin":"","legend":"\u003cp\u003eTop 10 hub genes(left: fish oil; right: forskolin)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4212015/v1/883b1ea2e19d1932ffb49dd2.png"},{"id":55762006,"identity":"1e156cdb-8704-4a39-beac-b2d56ef9ffa2","added_by":"auto","created_at":"2024-05-02 19:04:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":111902,"visible":true,"origin":"","legend":"\u003cp\u003eThe result of KEGG pathway enrichment analysis(left: fish oil; right: forskolin)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4212015/v1/b813ce26baccea91b364594a.png"},{"id":55760739,"identity":"467c1760-1e88-4cfe-b4ad-39784e094f49","added_by":"auto","created_at":"2024-05-02 18:56:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":129308,"visible":true,"origin":"","legend":"\u003cp\u003eOvarian steroidogenesis(fish oil),MAPK signaling pathway(forskolin)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4212015/v1/ed8db235514ab307b8e07a94.png"},{"id":70382518,"identity":"27f9e436-0b43-4002-bfc0-08c435726599","added_by":"auto","created_at":"2024-12-02 16:27:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1168710,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4212015/v1/0317f569-4584-4ea3-a821-9f2b70b35118.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploration and practice of \"disease—drug\" potential association prediction based on \"Swanson framework+Bioinformatics\"—a case study of Autism Spectrum Disorder","fulltext":[{"header":"Introduction","content":"\u003cp\u003eData mining appeared in the late 1980s and was first developed in the field of databases, which is called Knowledge Discovery in Databases (KDD).\u003csup\u003e[1]\u003c/sup\u003e The concept of knowledge discovery was first formally proposed at the 11th International Joint Artificial Intelligence Conference held in the United States in 1989. Since then, knowledge discovery has begun to flourish.\u003c/p\u003e\n\u003cp\u003eThe so-called Literature-based Knowledge Discovery is a classical information science method that identifies effective, novel, potentially useful and ultimately understandable knowledge from the content of unrelated literature through literature mining to discover the cross domain knowledge transfer and implicit correlation.\u003csup\u003e[2]\u003c/sup\u003e This method was proposed by professor Swanson, a famous American information scientist, in 1985. It describes how to obtain the undiscovered implied association from two types of unrelated literature. The general idea is: if one published article reports the meaningful association between A and B, and the other reports the association between B and C, but there is no literature about the association between A and C, The new relationship between A and C can be obtained by considering the two literatures together. Professor Swanson developed a knowledge discovery tool based on the principle of this method and put forward two hypotheses. One is that eating fish oil may change some blood parameters to treat Raynaud\u0026apos;s syndrome,\u003csup\u003e[2]\u003c/sup\u003e and the other is that magnesium deficiency can lead to migraine.\u003csup\u003e[3]\u0026nbsp;\u003c/sup\u003eThese two hypotheses were later verified by clinical experiments.\u003csup\u003e[4,5]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOn this basis, many scientists continue to put forward new ideas. Gordon, a professor at the University of Michigan in the United States, and his collaborators successfully reproduced the scientific hypothesis of \u0026quot;the relationship between edible fish oil and Raynaud\u0026apos;s disease\u0026quot; and \u0026quot;the relationship between magnesium and migraine\u0026quot;,\u003csup\u003e[6,7]\u003c/sup\u003e and developed a set of methods for knowledge discovery based on computer retrieval. According to the model from source literature to intermediary literature and then to target literature, they were used to assist in knowledge discovery of unrelated literature. Weeber proposed a \u0026quot;two-step discovery model\u0026quot;, which successfully reproduced the relationship between Raynaud\u0026apos;s disease and fish oil, magnesium and migraine, and formally defined the two steps of the process of knowledge discovery as \u0026quot;open discovery\u0026quot; and \u0026quot;closed verification\u0026quot;, that is, the process of open knowledge discovery is to find the intermediate word B through A, and then find C; The process of closed knowledge discovery is a process of testing hypotheses, starting from A and C to find a common intermediate concept B. Stegmann and\u0026nbsp;Grohmann\u0026nbsp;verified the process of Swanson\u0026apos;s knowledge discovery by using co-occurrence word clustering analysis,\u003csup\u003e[8]\u003c/sup\u003e found eigenvalues based on the ratio of centripetality and density, and quickly determined the clustering of possible intermediate words and unrelated literature words. Hristovski et al. proposed a literature-based interactive biomedical discovery support system BITOLA,\u003csup\u003e[9]\u003c/sup\u003e which aims to discover the potential relationship between biomedical concepts (including MeSH (medical subject title) and human genes from HUGO) by mining MEDLINE database, so as to help biomedical researchers propose or verify new knowledge discoveries.\u003c/p\u003e\n\u003cp\u003eIn the above studies, Arrowsmith, a knowledge discovery tool, mainly selects intermediate concepts based on semantics and co-occurrence frequency. Gordon believes that the intermediary literature is best identified by absolute word frequency, and the target literature is best generated from the intermediary literature by using relative frequency. The BITOLA system developed by Hristovski mainly selects intermediate concepts based on MeSH vocabulary and its semantic types, while Johannes Stegmann and others mainly select intermediate concepts based on centripetality and density. It can be seen that in the Literature-based Knowledge Discovery, researchers have different methods of selecting intermediate concepts, and the purpose is to find a fulcrum to increase the accuracy of knowledge discovery. Although the intermediate concept mentioned above increases the diversity of entries for knowledge discovery, it also improves the accuracy of prediction. However, compared with a large number of concept groups, the prediction target is still large, and it is still not easy to quickly find intermediate concepts with higher accuracy.\u003c/p\u003e"},{"header":"2 Function and role of Bioinformatics","content":"\u003cp\u003eBioinformatics is a subject that studies the collection, processing, storage, dissemination, analysis and interpretation of biological information. It reveals the biological laws of a large number of complex biological data through the comprehensive use of biology, computer science and information technology.\u003csup\u003e[10]\u0026nbsp;\u003c/sup\u003eBioinformatics analysis is a method to explore biological related problems through the analysis of biological sequence, protein structure and literature data.\u003csup\u003e[11]\u003c/sup\u003e With the development of science and technology, traditional biological data (such as species basic data, physiological and biochemical data, trait genetics, environmental data, etc.) and various omics data (such as genome, transcriptome, proteome, metabolome, epigenome, phenotypic group, etc.) are accumulating, providing a data basis for knowledge discovery from the perspective of Bioinformatics. At the same time, massive data and complex background have led to the rapid development and application of machine learning, statistical data analysis and system description methods in Bioinformatics,\u003csup\u003e[12,13]\u003c/sup\u003e which can help researchers better understand gene expression profiles, realize gene function prediction, molecular structure relationship prediction,\u003csup\u003e[14]\u003c/sup\u003e and discover the hidden knowledge from massive biological data. Bioinformatics is often used in the biomedical field to study the hidden information of diseases or drugs in organisms. Hai et al. studied the predictive value of the molecular characteristics of drug target genes for the sensitivity of targeted drugs in gastric cancer, and found genes that can predict the prognosis of patients and the efficacy of targeted compounds.\u003csup\u003e[15]\u003c/sup\u003e Zhang et al. screened out the differentially expressed genes shared by important brain tissues related to heat stress based on geo database.\u003csup\u003e[16]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, based on omics data, Bioinformatics analysis was used to calculate differentially expressed genes, go functional enrichment, KEGG pathway enrichment, etc. by using statistical methods in R language, in order to find the potential knowledge or association hidden in biological genes.\u003c/p\u003e"},{"header":"3 Exploration and practice of using Bioinformatics as an intermediate concept to carry out Literature-based Knowledge Discovery ","content":"\u003cp\u003eIn the process of Literature-based Knowledge Discovery, the core is to determine the intermediate concept, and an accurate intermediate concept is the key to improve the efficiency of knowledge discovery.\u003c/p\u003e\n\u003cp\u003eAlthough the text source field in the latest version of Arrowsmith system has been extended to the fields of document title, subject words and abstract, and the text processing time has been shortened, its natural language processing function is relatively limited, and the number of intermediate concept results provided is large, so it is unable to accurately and quickly identify the required biomedical concepts. On this basis, BITOLA system can accurately extract biomedical concepts by introducing\u0026nbsp;MeSH\u0026nbsp;vocabulary and natural language processing technology to support semantic prediction for the discovery of specific relationship types of \"disease-gene\". However, due to its wide variety and large number, it is still unable to accurately identify effective biomedical concepts. How to effectively reduce the noise of intermediate concept set has been the goal of researchers for many years. To solve this problem, other systems have also adopted measures. For example, BITOLA uses association rules instead of co-occurrence word frequency to express the relevance of concepts, DAD (Drug-Ad verse drug reactions-Disease) system uses concept frequency to sort intermediate concepts in the open discovery process,\u0026nbsp;LitLinker\u0026nbsp;uses UMLS semantic network to filter, and uses association rule mining algorithm to determine association concepts, but the fact is that despite this, It is still unable to effectively solve the problem of too many interfering words. Therefore, the efficiency of knowledge discovery cannot be truly solved only through these original unprocessed traditional intermediate concepts.\u003c/p\u003e\n\u003cp\u003eOn the basis of traditional methods, if entity information that is crucial, informative, and more directional for a certain disease or drug is used as an intermediate concept, it will undoubtedly be a highly filtered primary traditional intermediate concept, which will greatly improve the credibility and accuracy of knowledge discovery results. These more accurate and reliable entity information can be obtained through Bioinformatics analysis, that is, through the processing, analysis and mining of biomolecular data, the specific Bioinformatics entities in deep level can be extracted. Compared with the traditional intermediate concept, a specific Bioinformatics entity covers more information and has higher directivity. If it is used as an intermediate concept to carry out Literature-based Knowledge Discovery, it will greatly improve the scientificity and efficiency of research and development, such as detecting disease-related genes (i.e., differentially expressed genes) according to the results of gene function analysis. Therefore, based on the BITOLA system, this study combined with Bioinformatics methods to determine the intermediate concept, put forward the knowledge discovery concept of \"Swanson framework+Bioinformatics\", and carried out the exploration and practice of knowledge discovery in unrelated literature, in order to improve the prediction efficiency(the technical roadmap is shown in Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Proposing \"Swanson framework+Bioinformatics\" knowledge discovery (referred to as \"BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e\")\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKnowledge discovery based on \"Swanson framework+Bioinformatics\", that is, Literature-based Knowledge Discovery based on the intermediate concept of Bioinformatics, refers to the use of important deep-seated information about organisms (such as\u0026nbsp;differentially expressed genes) obtained from Bioinformatics analysis as the intermediate concept of ABC model to explore the potential \"disease-drug\" relationship, referred to as \"BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e\". This study selected the disease Autism Spectrum Disorder (hereinafter referred to as ASD or auitsm) to explore the practice of knowledge discovery in the unrelated literature, in order to evaluate the feasibility of the concept and the accuracy of the prediction results.\u003c/p\u003e\n\u003cp\u003e3.1.1 BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e result sorting algorithm\u003c/p\u003e\n\u003cp\u003eThe algorithm follows the knowledge discovery algorithm of BITOLA system, that is, based on the association rules representing the known relationships between concepts and considering the background knowledge, a new relationship between concepts is proposed. In order to check the results as easily as possible, the related concepts are sorted. Related concepts Y can be sorted by association rule support (co-occurrence frequency), confidence or semantic type.\u003c/p\u003e\n\u003cp\u003eThe related concepts Z can be sorted by the following calculation formula:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"251\" height=\"40\"\u003e\u003c/p\u003e\n\u003cp\u003eThe ranking is calculated based on support, but it can also be calculated based on confidence. In this equation,\u0026nbsp;Z\u003csub\u003ek\u003c/sub\u003e is the concept of calculating its rank, S\u003csub\u003eXYi\u003c/sub\u003e and S\u003csub\u003eYiZk\u003c/sub\u003e are the support of association rule X → Y\u003csub\u003ei\u0026nbsp;\u003c/sub\u003eand\u0026nbsp;Y\u003csub\u003ei\u003c/sub\u003e → Z\u003csub\u003ek\u003c/sub\u003e, and m is the number of intermediate concepts Y.\u003c/p\u003e\n\u003cp\u003e3.1.2 Calculation of differentially expressed genes\u003c/p\u003e\n\u003cp\u003eBioinformatics analysis results include differentially expressed genes, GO functional enrichment, KEGG pathway enrichment, etc. Among them, differential expressed genes (DEGs) refer to genes with significant differences in RNA expression due to environment, time and other factors. For drug research, differential expressed genes analysis is very important, such as network pharmacology, an emerging discipline based on high-throughput omics data analysis, computer virtual computing and network database retrieval\u003csup\u003e17\u003c/sup\u003e, which can enable researchers to recognize the mechanism of drug action from a new perspective (differentially expressed genes); Yang et al. used high-throughput omics data to calculate differentially expressed genes, etc., applied biological big data to rapid rational drug design, inspiring and accelerating the discovery process of existing antifungal drugs; \u003csup\u003e[18]\u003c/sup\u003eDu screened potential biomarkers of Sjogren's syndrome by calculating differentially expressed genes and Hub genes, providing new insights into the pathogenesis of Sjogren's syndrome.\u003csup\u003e[19]\u0026nbsp;\u003c/sup\u003eDifferentially expressed genes are the basis of Bioinformatics analysis and drug research. Researchers can analyze the potential information of diseases and drugs, such as targets and biomarkers, through differentially expressed genes, providing researchers with new insights and new research directions. Therefore, differentially expressed genes are a key and necessary element in the research of “disease-drug” potential association. Taking them as intermediate concepts is an important basis for improving the scientificity and accuracy of knowledge discovery research. Therefore, this paper takes one of the results of Bioinformatics analysis, differentially expressed genes, as an example, and takes them as intermediate concepts to explore the practical effect of the new method \"BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e\" for Literature-based Knowledge Discovery.\u003c/p\u003e\n\u003cp\u003eThe calculation method of differentially expressed genes in this study is FC (fold change) algorithm. The principle of the algorithm is to calculate the multiple of the average expression level of genes in the two types of samples. If the value reaches the preset threshold (generally set to 2, which is greater than 1 or less than -1 in the logarithmic expression ratio based on 2), the gene is judged to be differentially expressed. The calculation formula is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"123\" height=\"48\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eFC represents the calculation method of differentially expressed genes; \u0026nbsp;is the average expression value of gene i in X samples; is the average expression value of gene i in Y samples.\u003c/p\u003e\n\u003cp\u003e3.1.3 Determination and analysis steps of literature collection\u003c/p\u003e\n\u003cp\u003eFor the construction of the initial concept set, this study uses the\u0026nbsp;BITOLA\u0026nbsp;system strategy, which extracts the concepts in the title, abstract and MeSH fields of PubMed related literature as the initial concepts.\u003c/p\u003e\n\u003cp\u003eFor intermediate concept sets, the large number of concept sets will cause great interference to the discovery of truly meaningful target concepts. BIOINF-ABC\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eknowledge discovery model in order to improve the quality of target concepts, the intermediate concept set is filtered by Bioinformatics methods. After determining the target disease or drug, this method needs to select the appropriate data in the gene expression database to achieve Bioinformatics analysis and obtain a specific intermediate concept set. Choose one of the differential genes, pathways or proteins in the intermediate concept set as the intermediate concept (Y) of this study. At the same time, on the basis of the target concept set, the results are still screened by combining Bioinformatics methods (such as protein interaction network and pathway analysis). Of course, different Bioinformatics analysis methods (such as differentially expressed genes, pathways, proteins or immune infiltrating cells) may be used for different intermediate concepts or target concepts, which greatly improves the efficiency of target concept hit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Practice of \"BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e\" Literature-based Knowledge Discovery: taking the discovery of potential relationship of “ASD-drugs\" as an example\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3.2.1 Differentially expressed genes calculation of ASD\u003c/p\u003e\n\u003cp\u003eThe GEO(Gene Expression Omnibus) database was selected as the data source to obtain the experimental genes of ASD. The R language limma program package was used to calculate the differentially expressed genes,\u003csup\u003e[20]\u0026nbsp;\u003c/sup\u003eand the intersection genes with opposite regulatory effects in the differentially expressed genes were removed. The screening conditions were :|log2 (Fold Change) |\u0026gt;0.5, P\u0026lt;0.05. 105 genes with significant differential expression of ASD were obtained, including 57 up-regulated and 48 down-regulated genes; The clusterprofiler package was used to analyze the KEGG pathway enrichment of significantly differentially expressed genes,\u003csup\u003e[21]\u003c/sup\u003e and 60 pathways enriched by up-regulated genes and 79 pathways enriched by down-regulated genes were obtained.\u003c/p\u003e\n\u003cp\u003e3.2.2 Take the concept of Bioinformatics (differentially expressed gene) as an intermediate concept to carry out knowledge discovery\u003c/p\u003e\n\u003cp\u003eFirst, take Autistic Disorder as the initial concept input, and look for the intermediate concept Y related to X (semantic type is \"Gene or Gene Product\"). The search results showed that there were 340 genes related to the Autistic Disorder, which intersected with the previously calculated ASD DEGs list. The differentially expressed genes obtained were\u0026nbsp;IFI6,LPL,BRWD2, which were the selected Y. Then find the relevant Z according to Y (semantic type is \"Organic Chemical/Pharmaceutical Substance\"), and the result is the list of Z drugs potentially associated with X disease. According to the calculation results, 594 drugs were found, and this study selected the top 50 drugs with the highest semantic frequency for subsequent research (see Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Results of BITOLA knowledge discovery system (top 50 drugs)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"674\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003eSerial number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eDrug name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003eSerial number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eDrug name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eHeparin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eTriiodothyronine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eRecombinant Insulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eTetrachlorodibenzodioxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eEnzyme Inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eMethionine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eInterferon Type II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eFish Oils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eAntilipemic Agents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eFatty Acids, Omega-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eRecombinant Cytokines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eorlistat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eDexamethasone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eRecombinant Interferon-gamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eSerine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eFenofibrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eSomatotropin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eBucladesine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eHypoglycemic Agents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eAnticholesteremic Agents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eRecombinant Interleukin-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eArginine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eFat Emulsions, Intravenous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eAmino Acids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eRecombinant Interferon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eSomatropin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eThiazolidinediones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003erosiglitazone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eInterferon Type I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eglucagon (rDNA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eGlycerol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eRecombinant Interleukin-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eRecombinant Interleukin-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eSodium Chloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eTetradecanoylphorbol Acetate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eTretinoin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eLecithin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eAntineoplastic Agents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eWeight Loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eHeparin, Low-Molecular-Weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003emitochondrial uncoupling protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eImmune Sera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003e4-diethoxyphosphorylmethyl-N-(4-bromo-2-cyanophenyl)benzamide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003ePolyethylene Glycols\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eInterferon-alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eProtamines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eOleic Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eAdenosine Triphosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.36106983655275%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.73402674591382%\"\u003e\n \u003cp\u003eIsoproterenol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.806835066864785%\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.915304606240714%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172362555720653%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.288261515601782%\"\u003e\n \u003cp\u003eForskolin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.144130757800891%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.578008915304606%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe method of Bioinformatics was used to find the \"disease-drug\" correlation in the results. Select the top 50 drugs for analysis and screening: exclude the results of drugs belonging to class I and drugs without experimental data in the GEO database, and finally get 16 drugs (see Table 2). Bioinformatics analysis of these drugs was carried out and compared with the Bioinformatics analysis results of autism.\u003c/p\u003e\n\u003cp\u003eUsing GEO database as the data source, the experimental genes of 16 drugs were obtained. The R language limma program package was used to calculate the differentially expressed genes,\u003csup\u003e[20]\u003c/sup\u003e and excel was used to remove the intersection genes with opposite regulatory effects from the calculated differentially expressed genes, and the R language program was used to average the expression of differentially expressed genes with the same regulatory effect; The clusterprofiler package was used to enrich the KEGG pathway of differentially expressed genes.\u003csup\u003e[21]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eBy comparing the DEGs with opposite expression of ASD and their enriched KEGG pathway, we found that the drugs with closer association with ASD, and the specific results are shown in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eComparison results of drugs and ASD (results showing opposite expression of drugs and ASD)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"615\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.87012987012987%\"\u003e\n \u003cp\u003eDrug Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003eNumber of experiments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.01948051948052%\"\u003e\n \u003cp\u003eDifferentially\u003c/p\u003e\n \u003cp\u003eexpressed \u0026nbsp;genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003eKEGG pathway (Autism up-Drug down)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.694805194805195%\"\u003e\n \u003cp\u003eKEGG pathway (Autism down-Drug up)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.87012987012987%\"\u003e\n \u003cp\u003eDexamethasone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.01948051948052%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.694805194805195%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.87012987012987%\"\u003e\n \u003cp\u003eGlycerol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.01948051948052%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.694805194805195%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.87012987012987%\"\u003e\n \u003cp\u003eInterferon-alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.01948051948052%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.694805194805195%\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.87012987012987%\"\u003e\n \u003cp\u003eOleic Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.01948051948052%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.694805194805195%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.87012987012987%\"\u003e\n \u003cp\u003eTriiodothyronine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.01948051948052%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.694805194805195%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.87012987012987%\"\u003e\n \u003cp\u003eMethionine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.01948051948052%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.694805194805195%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.87012987012987%\"\u003e\n \u003cp\u003eFish Oils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.01948051948052%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.694805194805195%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.87012987012987%\"\u003e\n \u003cp\u003eRecombinant Interferon-gamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.01948051948052%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.694805194805195%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.87012987012987%\"\u003e\n \u003cp\u003eFenofibrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.01948051948052%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.694805194805195%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.87012987012987%\"\u003e\n \u003cp\u003eBucladesine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.01948051948052%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.694805194805195%\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.87012987012987%\"\u003e\n \u003cp\u003eArginine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.01948051948052%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.694805194805195%\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.87012987012987%\"\u003e\n \u003cp\u003eSomatropin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.01948051948052%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.694805194805195%\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.87012987012987%\"\u003e\n \u003cp\u003eRosiglitazone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.01948051948052%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.694805194805195%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.87012987012987%\"\u003e\n \u003cp\u003eGlucagon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.01948051948052%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.694805194805195%\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.87012987012987%\"\u003e\n \u003cp\u003eTretinoin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.01948051948052%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.694805194805195%\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.87012987012987%\"\u003e\n \u003cp\u003eForskolin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.636363636363637%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.01948051948052%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.694805194805195%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Number of experiments: differentially expressed genes were calculated for a control experiment consisting of 3 or more groups. The number of experiments represented the number of control experiments.\u003c/p\u003e\n\u003cp\u003eFor the above 16 drugs, after comprehensive consideration of the number of experiments in the data set, the complexity of data processing, the number of differentially expressed genes and KEGG pathways, it was found that (1) Although triiodothyronine,recombinant interferon\u0026nbsp;γ,glucagon, growth hormone and bradysin are dominant in differentially expressed genes, they are not considered due to the small number of experiments; (2) Because the number of differentially expressed genes overlapped with ASD is too small, dexamethasone, glycerol, interferonα,oleic acid, methionine, fenofibrate, arginine, rosiglitazone, retinoic acid and other drugs will not be considered; (3) Both fish oil and forskolin have absolute advantages in terms of the number of experiments, differentially expressed genes, pathways and so on. Therefore, based on the Literature-based Knowledge Discovery results of \"BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e\", this study believes that fish oil and forskolin have high potential \"drug-disease\" association credibility for ASD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Bioinformatics reverse verification for results of \"BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e\" Literature-based Knowledge Discovery\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn the basis of the above research, this study analyzed the two drugs and ASD respectively by Bioinformatics method, verified the above analysis results from the Bioinformatics level, and made a deeper comparison and analysis of the two drugs. The first is to construct the Protein-Protein Interaction network of significantly different genes and calculate the key genes. Previously, the opposite part of the fish oil/forskolin differentially expressed genes has been removed and screened under the condition of\u0026nbsp;|log2(Fold Change)|\u0026gt;0.5. 1129 significant differentially expressed genes in fish oil were obtained, including 529 up-regulated genes and 600 down-regulated genes; There were 1164 significant differentially expressed genes for forskolin, including 715 up-regulated genes and 449 down-regulated genes. Upload significant differentially expressed genes to\u0026nbsp;STRING v11.0(https://string-db.org/), an online analysis website, to conduct Protein-Protein Interaction (PPI) network analysis, and take the confidence\u0026gt;0.4 as the threshold for screening.\u003csup\u003e[22]\u003c/sup\u003e Key genes are highly correlated genes in PPI network. In this study, the key genes are the top 10 genes with the highest frequency appear in the PPI network relationship. The CytoHubba plug-in\u003csup\u003e23\u0026nbsp;\u003c/sup\u003eof the Cytoscape software\u003csup\u003e24\u0026nbsp;\u003c/sup\u003ewill rank proteins according to their properties in the network, and provide 12 topological analysis methods, such as\u0026nbsp;Degree, Edge Percolated Component (EPC), Maximum Neighborhood Component (MNC), and score and rank proteins according to the corresponding algorithms. In this study, the\u0026nbsp;CytoHubba\u0026nbsp;plug-in of Cytoscape software was used to analyze the results of PPI network. The top 10 proteins of 12 algorithms were output, and the top 10 proteins of frequency were counted as core genes. The second is enrichment analysis. Previously, the R language clusterprofiler package has been used to enrich the KEGG pathway of differentially expressed genes.\u003csup\u003e[21]\u003c/sup\u003e Here, the R language is used to visualize the clustering results.\u003c/p\u003e\n\u003cp\u003eComparative analysis of the significant differentially expressed genes and KEGG pathway with the opposite regulatory effect of ASD and fish oil/forskolin showed that in terms of the differentially expressed genes,\u003csup\u003e[22]\u003c/sup\u003e 12 topological analysis methods such as Degree, EPC, MNC, DMNC and MCC of the CytoHubba plug-in in Cytoscape software were used to analyze the PPI network, and the frequency statistics of the top 10 proteins in each output score of the 12 algorithms were performed. The top 10 proteins were identified as core genes with important regulatory roles (see Figure 2).\u003c/p\u003e\n\u003cp\u003eThe R language clusterprofiler package was used to enrich the KEGG pathway of the significant differentially expressed genes between fish oil and forskolin. The results showed that fish oil had 285 pathways enriched by up-regulated genes and 294 pathways enriched by down-regulated genes. Screening was performed with a threshold of P\u0026lt;0.05, with 34 pathways enriched by up-regulated genes and 22 pathways enriched by down-regulated genes (see Figure 3 for some pathways). Additionally, forskolin had 300 pathways enriched by up-regulated genes and 236 pathways enriched by down-regulated genes, screened with a threshold of P\u0026lt;0.05. There are 45 pathways enriched by up-regulated genes and 10 pathways enriched by down-regulated genes (some pathways are shown in Figure 3).\u003c/p\u003e\n\u003cp\u003eAmong the pathways enriched by genes with significant differences in fish oil (P\u0026lt;0.05), there were three pathways containing both up-regulated and down-regulated genes:\u0026nbsp;Neuroactive ligand-receptor interaction,\u0026nbsp;Purine metabolism, and\u0026nbsp;Biosynthesis of unsaturated fatty acids; The up-regulated genes enrichment pathways are mainly involved in cAMP signaling pathway, Ras signaling pathway, Cell adhesion molecules and other related functions or processes; Down-regulated genes enrichment pathways are mainly involved in p53 signaling pathway, Fatty acid metabolism, Cell cycle and other related functions or processes.\u003c/p\u003e\n\u003cp\u003eAmong the pathways enriched by genes with significant differences in forskolin (P\u0026lt;0.05), two pathways contain both up-regulated genes and down-regulated genes:\u0026nbsp;Transcriptional misregulation in cancer\u0026nbsp;and MAPK signaling pathway; The pathways of up-regulated genes enrichment are mainly involved in PPAR signaling pathway, p53 signaling pathway, IL-17 signaling pathway and other related functions or processes; Down -regulated genes enrichment pathways are mainly involved in Biosynthesis of amino acid, Nucleotide metabolism, Glycine, serine and threonine metabolism and other related functions or processes.\u003c/p\u003e\n\u003cp\u003eIt can be seen that among the significant differentially expressed genes screened by ASD and fish oil, there are four identical genes, including two genes with opposite regulatory effects:\u0026nbsp;PTPRR and RASD1. The protein encoded by\u0026nbsp;PTPRR\u0026nbsp;gene is a member of Protein Tyrosine Phosphatase (PTP) family. PTP is a signal molecule that regulates various cellular processes, including cell growth, differentiation, mitotic cycle and carcinogenic transformation.\u0026nbsp;RASD1\u0026nbsp;gene encodes a member of the small gtpase Ras superfamily. The coding protein is an activator of G protein signal transduction and serves as a direct nucleotide exchange factor of\u0026nbsp;Gi-Go\u0026nbsp;protein. Among the significant differentially expressed genes screened by ASD and forskolin, there were 8 identical genes, and 2 genes with opposite regulatory effects:\u0026nbsp;RASD1 and DUSP14. Bispecific phosphatase DUSP is characterized by its ability to dephosphorylate tyrosine and serine/threonine residues. They are considered to be the main regulators of key signaling pathways. Among the KEGG pathways enriched by the significant differentially expressed genes screened from ASD and fish oil, 126 pathways were enriched by genes with opposite regulatory effects, among which the pathway satisfying P\u0026lt;0.05 in ASD and fish oil was 0, and the pathway satisfying P\u0026lt;0.1 was 1: Ovarian steroidogenesis. This pathway contains two ASD genes: PLA2G4B and\u0026nbsp;FSHB; There are four fish oil genes:\u0026nbsp;ACOT1, ACOT2, ADCY4 and ACOT4. These genes are mainly involved in Reproductive organ development, Fatty acid metabolism and other processes. Ovarian steroidogenesis: ovarian steroids, 17-βestradiol (E2) and progesterone (P4) are essential for normal uterine function, the establishment and maintenance of pregnancy and the development of mammary gland. The above six genes are shown in the ovarian steroidogenesis pathway map, which shows that these genes are mainly involved in GnRH signaling pathway.\u003c/p\u003e\n\u003cp\u003eAmong the KEGG pathways enriched by the significant differentially expressed genes screened by ASD and forskolin, 129 pathways were enriched by genes with opposite regulatory effects, among which one pathway satisfying P\u0026lt;0.05 in ASD and forskolin simultaneously: MAPK signaling pathway. This pathway contains three ASD genes:\u0026nbsp;GADD45G, PTPRR and CSF1R; There are 11 forskolin genes:\u0026nbsp;FLT3LG, RRAS2, RPS6KA2, EPHA2, CACNB4, ATF4, CSF1, FLNC, ANGPT2, GADD45A and DDIT3. These genes are mainly involved in cellular processes and inflammatory reactions. MAPK signaling pathway: mitogen activated protein kinase (MAPK) cascade is a highly conserved module involved in various cell functions, including cell proliferation, differentiation and migration. The above 14 genes are represented in the MAPK signaling pathway map, which shows that these genes are mainly involved in the classical MAP kinase pathway.\u003c/p\u003e\n\u003cp\u003eThe KEGG pathway of Ovarian steroidogenesis and MAPK signaling pathway are shown in Figure 4.\u003c/p\u003e\n\u003cp\u003eIn summary, 10 key genes were selected from the differentially expressed genes as the core of subsequent text verification. The results of pathway enrichment analysis showed that fish oil was involved in a key pathway of autism, namely Ovarian steroidogenesis pathway, which was involved in GnRH signal transduction; Forskolin is also involved in a key pathway of autism, namely MAPK signaling pathway, and its classical MAP kinase pathway. Therefore, the results of knowledge discovery based on \"BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e\" have achieved good verification results in the level of Bioinformatics analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003cstrong\u003eext verification of results of \"BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e\" Literature-based Knowledge Discovery\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the domain knowledge score method was used to verify the effectiveness of fish oil/forskolin targets in Chinese and English databases. The specific operation is as follows: take 10 key genes as key targets, search in the English database PubMed with \"autism\" and \"key targets\" as the key words, and search in the Chinese database CNKI and Wanfang database with \"autism\" and \"key targets\" as the key words, record the relevant search results and calculate their cumulative scores. Table 3 shows the retrieval results of the above key targets that are mainly involved in inflammatory response, cell cycle progression and other related processes in ASD patients. The results showed that fish oil and forskolin were highly correlated with ASD, especially forskolin.\u003c/p\u003e\n\u003cp\u003eTricholaryngin is a direct AC/cAMP/CREB activator, which is isolated from Angelica dahurica and has various neuroprotective properties. A number of studies have shown that the application of forskolin in the treatment of ASD is feasible. Alharbi et al. have shown that forskolin has been proved in their laboratory that it can directly activate adenylate cyclase (AC) and reverse neurodegeneration related to the progression of autism, multiple sclerosis, ALS and Huntington's disease.\u003csup\u003e[25]\u003c/sup\u003e Mehan et al. have shown that forskolin can alleviate neuronal mitochondrial dysfunction and improve neurological symptoms in autism rats.\u003csup\u003e[26]\u003c/sup\u003e Chi have shown that the agonist forskolin may regulate FMR1 gene mainly through the cAMP signaling pathway through the overlapping sites in the promoter region of FMR1, the pathogenic gene of fragile X syndrome.\u003csup\u003e[27]\u003c/sup\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"669\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" style=\"width: 59.538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e List of text verification results for 10 key targets based on domain knowledge scores\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.208955223880594%\" colspan=\"3\" style=\"width: 35.0249%;\"\u003e\n \u003cp\u003eFish oil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.95522388059702%\" colspan=\"3\" style=\"width: 34.0697%;\"\u003e\n \u003cp\u003eForskolin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.343283582089553%\" style=\"width: 10.7197%;\"\u003e\n \u003cp\u003eTarget name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.62686567164179%\" style=\"width: 15.8143%;\"\u003e\n \u003cp\u003eCorrelation effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238805970149254%\" style=\"width: 8.4909%;\"\u003e\n \u003cp\u003eDomain knowledge score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.298507462686567%\" style=\"width: 8.597%;\"\u003e\n \u003cp\u003eTarget name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.671641791044777%\" style=\"width: 16.5572%;\"\u003e\n \u003cp\u003eCorrelation effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.985074626865671%\" style=\"width: 8.9154%;\"\u003e\n \u003cp\u003eDomain knowledge score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.343283582089553%\" style=\"width: 10.7197%;\"\u003e\n \u003cp\u003eCCNA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.62686567164179%\" style=\"width: 15.8143%;\"\u003e\n \u003cp\u003eRegulator of the cell cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238805970149254%\" style=\"width: 8.4909%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.298507462686567%\" style=\"width: 8.597%;\"\u003e\n \u003cp\u003eCCNA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.671641791044777%\" style=\"width: 16.5572%;\"\u003e\n \u003cp\u003eRegulator of the cell cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.985074626865671%\" style=\"width: 8.9154%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.343283582089553%\" style=\"width: 10.7197%;\"\u003e\n \u003cp\u003eCCNB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.62686567164179%\" style=\"width: 15.8143%;\"\u003e\n \u003cp\u003eInvolved in controlling the G2 / M transition phase of the cell cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238805970149254%\" style=\"width: 8.4909%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.298507462686567%\" style=\"width: 8.597%;\"\u003e\n \u003cp\u003eCCNB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.671641791044777%\" style=\"width: 16.5572%;\"\u003e\n \u003cp\u003eInvolved in controlling the G2 / M transition phase of the cell cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.985074626865671%\" style=\"width: 8.9154%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.343283582089553%\" style=\"width: 10.7197%;\"\u003e\n \u003cp\u003eIL6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.62686567164179%\" style=\"width: 15.8143%;\"\u003e\n \u003cp\u003eAssociated with a variety of inflammatory - related disease states\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238805970149254%\" style=\"width: 8.4909%;\"\u003e\n \u003cp\u003e243+49+16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.298507462686567%\" style=\"width: 8.597%;\"\u003e\n \u003cp\u003eCXCL8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.671641791044777%\" style=\"width: 16.5572%;\"\u003e\n \u003cp\u003eA major mediator of the inflammatory response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.985074626865671%\" style=\"width: 8.9154%;\"\u003e\n \u003cp\u003e9+1+0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.343283582089553%\" style=\"width: 10.7197%;\"\u003e\n \u003cp\u003eESR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.62686567164179%\" style=\"width: 15.8143%;\"\u003e\n \u003cp\u003eRegulate the transcription of many estrogen induced genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238805970149254%\" style=\"width: 8.4909%;\"\u003e\n \u003cp\u003e16+0+0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.298507462686567%\" style=\"width: 8.597%;\"\u003e\n \u003cp\u003eVEGFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.671641791044777%\" style=\"width: 16.5572%;\"\u003e\n \u003cp\u003eInduce endothelial cell proliferation, promote cell migration and inhibit cell apoptosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.985074626865671%\" style=\"width: 8.9154%;\"\u003e\n \u003cp\u003e14+1+0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.343283582089553%\" style=\"width: 10.7197%;\"\u003e\n \u003cp\u003eIL1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.62686567164179%\" style=\"width: 15.8143%;\"\u003e\n \u003cp\u003eAn important agent of inflammatory response and is involved in a variety of cellular activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238805970149254%\" style=\"width: 8.4909%;\"\u003e\n \u003cp\u003e15+1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.298507462686567%\" style=\"width: 8.597%;\"\u003e\n \u003cp\u003eFOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.671641791044777%\" style=\"width: 16.5572%;\"\u003e\n \u003cp\u003eRegulator of cell proliferation, differentiation and transformation. Associated with apoptotic cell death.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.985074626865671%\" style=\"width: 8.9154%;\"\u003e\n \u003cp\u003e109+12+1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.343283582089553%\" style=\"width: 10.7197%;\"\u003e\n \u003cp\u003eIL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.62686567164179%\" style=\"width: 15.8143%;\"\u003e\n \u003cp\u003ePlay a pleiotropic role in immune regulation and inflammation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238805970149254%\" style=\"width: 8.4909%;\"\u003e\n \u003cp\u003e98+20+11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.298507462686567%\" style=\"width: 8.597%;\"\u003e\n \u003cp\u003eHDAC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.671641791044777%\" style=\"width: 16.5572%;\"\u003e\n \u003cp\u003eInvolved in cell proliferation and differentiation, cell growth and apoptosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.985074626865671%\" style=\"width: 8.9154%;\"\u003e\n \u003cp\u003e10+2+7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.343283582089553%\" style=\"width: 10.7197%;\"\u003e\n \u003cp\u003eBRCA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.62686567164179%\" style=\"width: 15.8143%;\"\u003e\n \u003cp\u003eMaintain genomic stability and act as a tumor suppressor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238805970149254%\" style=\"width: 8.4909%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.298507462686567%\" style=\"width: 8.597%;\"\u003e\n \u003cp\u003eIGF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.671641791044777%\" style=\"width: 16.5572%;\"\u003e\n \u003cp\u003eA member of a family of proteins involved in mediating growth and development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.985074626865671%\" style=\"width: 8.9154%;\"\u003e\n \u003cp\u003e74+4+3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.343283582089553%\" style=\"width: 10.7197%;\"\u003e\n \u003cp\u003eCCND1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.62686567164179%\" style=\"width: 15.8143%;\"\u003e\n \u003cp\u003eInvolved in cell cycle processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238805970149254%\" style=\"width: 8.4909%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.298507462686567%\" style=\"width: 8.597%;\"\u003e\n \u003cp\u003eCYCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.671641791044777%\" style=\"width: 16.5572%;\"\u003e\n \u003cp\u003eInvolved in the initiation of apoptosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.985074626865671%\" style=\"width: 8.9154%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.343283582089553%\" style=\"width: 10.7197%;\"\u003e\n \u003cp\u003eBUB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.62686567164179%\" style=\"width: 15.8143%;\"\u003e\n \u003cp\u003ePlay a central role in mitosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238805970149254%\" style=\"width: 8.4909%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.298507462686567%\" style=\"width: 8.597%;\"\u003e\n \u003cp\u003eCEBPB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.671641791044777%\" style=\"width: 16.5572%;\"\u003e\n \u003cp\u003ePlay an important role in regulating genes involved in immune and inflammatory responses, among other processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.985074626865671%\" style=\"width: 8.9154%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.343283582089553%\" style=\"width: 10.7197%;\"\u003e\n \u003cp\u003eMKI67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.62686567164179%\" style=\"width: 15.8143%;\"\u003e\n \u003cp\u003eAssociated with cell proliferation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238805970149254%\" style=\"width: 8.4909%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.298507462686567%\" style=\"width: 8.597%;\"\u003e\n \u003cp\u003ePPARGC1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.671641791044777%\" style=\"width: 16.5572%;\"\u003e\n \u003cp\u003eRegulate the genes involved in energy metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.985074626865671%\" style=\"width: 8.9154%;\"\u003e\n \u003cp\u003e3+0+0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.343283582089553%\" style=\"width: 10.7197%;\"\u003e\n \u003cp\u003eNTRK2(TRKB )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.62686567164179%\" style=\"width: 15.8143%;\"\u003e\n \u003cp\u003eSend signals to the cell that lead to differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238805970149254%\" style=\"width: 8.4909%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.298507462686567%\" style=\"width: 8.597%;\"\u003e\n \u003cp\u003ePTGS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.671641791044777%\" style=\"width: 16.5572%;\"\u003e\n \u003cp\u003eResponsible for prostaglandin biosynthesis involved in inflammation and mitosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.985074626865671%\" style=\"width: 8.9154%;\"\u003e\n \u003cp\u003e23+1+0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.343283582089553%\" style=\"width: 10.7197%;\"\u003e\n \u003cp\u003ePLK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.62686567164179%\" style=\"width: 15.8143%;\"\u003e\n \u003cp\u003eInvolved in mitosis and apoptosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238805970149254%\" style=\"width: 8.4909%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.298507462686567%\" style=\"width: 8.597%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.671641791044777%\" style=\"width: 16.5572%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.985074626865671%\" style=\"width: 8.9154%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.343283582089553%\" style=\"width: 10.7197%;\"\u003e\n \u003cp\u003eTOP2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.62686567164179%\" style=\"width: 15.8143%;\"\u003e\n \u003cp\u003eInvolved in processes such as chromosome condensation, chromatid separation, and relief of torsional stress during DNA transcription and replication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.238805970149254%\" style=\"width: 8.4909%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.298507462686567%\" style=\"width: 8.597%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.671641791044777%\" style=\"width: 16.5572%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.985074626865671%\" style=\"width: 8.9154%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn addition, mitochondrial dysfunction is one of the important pathological markers of autism, and mitochondrial dysfunction in autism is associated with decreased ATP levels due to decreased levels of cyclic adenylate monophosphate. The diterpenoid tricholaryngins extracted from tricholaryngins can regulate various physiological functions of cells by increasing cyclic adenylate monophosphate and up-regulating adenylate cyclase. In order to study the neuroprotective effect of tricholaryngine on autism,\u0026nbsp;Mehan\u0026nbsp;et al. (2020) from ISF School of Pharmacy, India, administered adenylate cyclase activator tricholaryngine intragastally to model rats with autism for 15 days at doses of 10, 20 and 30mg/kg. It was found that tricholaryngine can dose-dependently improve neuronal mitochondrial dysfunction, one of the important pathological markers of autism, and reduce the levels of pro-inflammatory cytokines, oxidative stress, and lipid biomarkers, further demonstrating the potential of adenylate cyclase activators in the treatment of autism (Chi, 2012). Fish oil supplementation may improve hyperactivity, lethargy, and stereotyping in people with autism, but the available clinical data is too limited to draw definitive conclusions.The above scientific research results once again demonstrate the potential therapeutic effects of these two drugs on autism.\u003c/p\u003e\n\u003cp\u003eIt can be seen that from the perspective of experimental evidence recorded in the literature, the results of knowledge discovery based on \"BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e\" have also been well verified in the literature set, which proves that the scientific hypothesis (i.e. disease X-drug Z relationship) obtained by this method has a good experimental basis, so the possibility, feasibility and reliability of using this method to predict the potential drug disease relationship are high.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Result analysis of \"BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e\" Literature-based Knowledge Discovery\u003c/h2\u003e \u003cp\u003eThis study used the BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e model to carry out the Literature-based Knowledge Discovery, and predicted the association between forskolin and fish oil in the field of ASD, namely \"forskolin-ASD\" and \"fish oil-ASD\". The two groups of \"drug-disease\" association results showed good scientificity in the Bioinformatics analysis results, and also showed high accuracy and reliability in the text verification. From the above analysis results, BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e model has good practicability, applicability and accuracy in the biomedical field of \"drug-disease\" association prediction. Therefore, it is feasible and efficient to use the results of Bioinformatics analysis as an intermediate concept for knowledge discovery on the basis of Literature-based Knowledge Discovery. Secondly, the BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e model proposed in this study is scientific and can be used for knowledge discovery. At the same time, it also provides a new research idea for the future study of \u0026ldquo;drug-disease\u0026rdquo; relationship.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Efficiency analysis of \"BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e\" Literature-based Knowledge Discovery\u003c/h2\u003e \u003cp\u003eAmong the top 50 drugs retrieved by BITOLA system, fish oil ranked 29th and forskolin ranked 50th. Assuming that the full score of 50 points is given to each drug according to the order of drug occurrence, the drug ranking first is 50 points, and the drug ranking 50 is 1 point, including 22 points for fish oil and 1 point for Forskolin. At the same time, Bioinformatics analysis of these 50 drugs showed that fish oil and forskolin ranked the top 2 in the analysis results, and the results of BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e model were also scored, with 50 points for the first drug and 49 points for the second drug, 50 points for fish oil and 49 points for forskolin (see Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIf the drug prediction accuracy is:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\text{Y}=\\frac{{\\text{x}}_{\\text{a}}+{\\text{x}}_{\\text{b}}}{50\\times 2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThen, the accuracy of BITOLA system for calculating fish oil and Forskolin is Y\u003csub\u003eBITOLA\u003c/sub\u003e= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{22+1}{50\\times 2}\\)\u003c/span\u003e\u003c/span\u003e =23%;\u003c/p\u003e \u003cp\u003eThe accuracy of BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e model in this study was Y\u003csub\u003eBIOINF\u0026minus;ABC\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{50+49}{50\\times 2}\\)\u003c/span\u003e\u003c/span\u003e =99%ཡ\u003c/p\u003e \u003cp\u003eIt can be seen from the comparison of the accuracy of prediction that the accuracy of BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e model is 76% higher than that calculated by BITOLA system alone. Therefore, the BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e model proposed in this study has high accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Exploration of \"BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e\" Literature-based Knowledge Discovery application fields\u003c/h2\u003e \u003cp\u003eThe BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e model in this study is based on the BITOLA system. It is based on Medline database and retrieved by using the results of Bioinformatics analysis as an intermediate concept. It has achieved good results in the \u0026ldquo;disease-gene-drug\u0026rdquo; association. Therefore, the model has good applicability in the biomedical field.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn conclusion, based on Swanson's Literature-based Knowledge Discovery and Bioinformatics, this study proposed the BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e model. This study found that fish oil and forskolin had a certain therapeutic effect on ASD, which verified the scientificity and accuracy of the BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e model, and provided new research ideas and research directions for future drug research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our deep appreciation to the participants who responded to this research.We also would like to express our deep appreciation to all the databases and researchers who provided the research data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.Y.H. analyzed the data, wrote the paper, and revised the paper. Y.Y.Y. analyzed the data, and wrote the paper. Z.X.Y. analyzed the data.Z.H.X. organized original data. Q.B.Q. analyzed the data. L.X.C. provided methods guidance. Y.Q. provided strategic design and methods guidance.All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study were all from publicly available databases.The bioinformatics data used in this study were sourced from the GEO database, with datasets consisting of\u003c/p\u003e\n\u003cp\u003eGSE59927, GSE45577, GSE43723, GSE52684, GSE58062, GSE28482, GSE62673, GSE73195, GSE70922,\u003c/p\u003e\n\u003cp\u003eGSE50945, GSE48368, GSE56166,GSE46914, GSE59927, GSE5258, GSE137033, GSE124935, GSE59927,\u003c/p\u003e\n\u003cp\u003eGSE68144, GSE59927, GSE83891, GSE73385, GSE68266, GSE42438, GSE22631.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by\u0026nbsp;the National Social Science Foundation of China(Nos.20BTQ064).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interset declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe have no conflict of interest in this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang, G. 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Mechanism research: Re-expression of Fragile X Mental Retardation 1 gene induced by adenylate cyclase activator (Master\u0026apos;s Thesis). \u003cem\u003eGuangdong: Southern Medical University\u003c/em\u003e.(2012)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Literature-based Knowledge Discovery, Autism Spectrum Disorders, Differentially Expressed Genes, Drug Discovery","lastPublishedDoi":"10.21203/rs.3.rs-4212015/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4212015/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eCompared to traditional intermediate concepts, specific bioinformatics entities are more informative and higher directional. This study is based on the BITOLA system and combines bioinformatics methods to determine the intermediate concept which is key to improve efficiency of Literature-based Knowledge Discovery, proposes the concept of \"Swanson framework\u0026thinsp;+\u0026thinsp;Bioinformatics\", and conducts practice of Literature-based Knowledge Discovery to improve the scientificity and efficiency of research and development.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFirstly, detected the disease related genes (i.e. differentially expressed genes) according to the results of gene functional analysis as intermediate concepts to carry out Literature-based Knowledge Discovery. Taking the disease \"Autism Spectrum Disorder(ASD)\" as an example, the potential \"disease-drug\" association was predicted, and the predicted drugs were verified from the perspective of bioinformatics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTwo drugs potentially associated with ASD were found: fish oil and forskolin, which were closely related to ASD in bioinformatics analysis results and literature verification.The two \"disease-drug\" association results showed better scientificity. The BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e model improves the accuracy of calculations by 76% compared to using the BITOLA system alone.In addition, it also shows high accuracy and credibility in literature verification.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe BIOINF-ABC\u003csup\u003e+\u003c/sup\u003e model based on the \"Swanson framework\u0026thinsp;+\u0026thinsp;Bioinformatics\" has good practicality, applicability, and accuracy in conducting \"disease-drug\" association prediction in the biomedical field, and can be used for mining \"disease-drug\" relationships.\u003c/p\u003e","manuscriptTitle":"Exploration and practice of \"disease—drug\" potential association prediction based on \"Swanson framework+Bioinformatics\"—a case study of Autism Spectrum Disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-02 18:56:50","doi":"10.21203/rs.3.rs-4212015/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvited","content":"","date":"2024-04-25T07:49:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-25T07:47:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-04-03T10:30:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b18b6634-a0c5-4d81-8402-2f8789e001f2","owner":[],"postedDate":"May 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":31316210,"name":"Health sciences/Medical research"},{"id":31316211,"name":"Health sciences/Diseases/Psychiatric disorders/Autism spectrum disorders"},{"id":31316212,"name":"Biological sciences/Drug discovery"},{"id":31316213,"name":"Biological sciences/Drug discovery/Drug screening"},{"id":31316214,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":31316215,"name":"Biological sciences/Computational biology and bioinformatics/Data mining"},{"id":31316216,"name":"Biological sciences/Computational biology and bioinformatics/Functional clustering"},{"id":31316217,"name":"Biological sciences/Computational biology and bioinformatics/Gene ontology"},{"id":31316218,"name":"Biological sciences/Computational biology and bioinformatics/Gene regulatory networks"},{"id":31316219,"name":"Biological sciences/Computational biology and bioinformatics/Genome informatics"},{"id":31316220,"name":"Biological sciences/Computational biology and bioinformatics/High throughput screening"},{"id":31316221,"name":"Biological sciences/Computational biology and bioinformatics/Literature mining"},{"id":31316222,"name":"Biological sciences/Computational biology and bioinformatics/Predictive medicine"}],"tags":[],"updatedAt":"2024-12-02T16:00:55+00:00","versionOfRecord":{"articleIdentity":"rs-4212015","link":"https://doi.org/10.1038/s41598-024-79587-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-11-28 15:57:17","publishedOnDateReadable":"November 28th, 2024"},"versionCreatedAt":"2024-05-02 18:56:50","video":"","vorDoi":"10.1038/s41598-024-79587-6","vorDoiUrl":"https://doi.org/10.1038/s41598-024-79587-6","workflowStages":[]},"version":"v1","identity":"rs-4212015","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4212015","identity":"rs-4212015","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}