Optimizing a Interpretable Diagnostic Model for Colorectal Cancer Based on Yin Deficiency Pattern Characteristic Genes Using 21 Machine Learning Algorithms and Bayesian Opitimization

Optimizing a Interpretable Diagnostic Model for Colorectal Cancer Based on Yin Deficiency Pattern Characteristic Genes Using 21 Machine Learning Algorithms and Bayesian Opitimization | 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 Optimizing a Interpretable Diagnostic Model for Colorectal Cancer Based on Yin Deficiency Pattern Characteristic Genes Using 21 Machine Learning Algorithms and Bayesian Opitimization Yuqing Li, Zhongquan Huang, Xiang Liu, Shun Xu, Zhuoni Chen, Ning Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3430999/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract In this study, we aimed to identify a core set of Yin Deficiency Pattern (YDP) genes for predicting colorectal cancer (CRC) and to construct reliable machine learning models optimized by Optuna. Comprehensive analysis was performed on nine datasets, totaling 1,680 samples. A CRC diagnostic prediction model was developed by comparing 21 machine learning classification models with Optuna hyperparameter optimization and validated across six independent external cohorts. Additionally, the expression patterns of the core diagnostic genes were experimentally validated. Linear Discriminant Analysis (LDA), along with four other machine learning models (please specify these models), ranked as the top five performing models across six cohorts, demonstrating superior performance with AUC values exceeding 0.99 and all other performance metrics surpassing 0.899. This study marks the first utilization of four specific novel machine learning models (again, please specify these models) in CRC diagnosis. The robust performance of the top models across multiple external validation sets underscores the reliability and generalizability of our diagnostic model. These results hold potential implications for the development of personalized medicine approaches in CRC treatment, offering a new avenue for early detection and prognosis improvement. Biological sciences/Systems biology Health sciences/Molecular medicine Bayasian Optimization Gaussian Process Machine learning Colorectal Cancer In vitro experiment Yin deficiency pattern Multiple external validations Full Text Additional Declarations No competing interests reported. Supplementary tables 1-8 are not available with this version. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Dec, 2023 Reviews received at journal 17 Dec, 2023 Reviewers agreed at journal 30 Nov, 2023 Reviewers agreed at journal 27 Nov, 2023 Reviewers invited by journal 27 Nov, 2023 Editor assigned by journal 22 Nov, 2023 Editor invited by journal 11 Oct, 2023 Submission checks completed at journal 11 Oct, 2023 First submitted to journal 11 Oct, 2023 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-3430999","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":239362163,"identity":"a6eb4d6a-fc6d-4cc1-bc33-0b1b013acbdd","order_by":0,"name":"Yuqing Li","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuqing","middleName":"","lastName":"Li","suffix":""},{"id":239362164,"identity":"7c2985fa-78dd-4d32-87db-e4b53a7083d0","order_by":1,"name":"Zhongquan Huang","email":"","orcid":"","institution":"Guangzhou University of Chinese 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Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bayasian Optimization, Gaussian Process, Machine learning, Colorectal Cancer, In vitro experiment, Yin deficiency pattern, Multiple external validations","lastPublishedDoi":"10.21203/rs.3.rs-3430999/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3430999/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In this study, we aimed to identify a core set of Yin Deficiency Pattern (YDP) genes for predicting colorectal \r\ncancer (CRC) and to construct reliable machine learning models optimized by Optuna. Comprehensive analysis was performed \r\non nine datasets, totaling 1,680 samples. A CRC diagnostic prediction model was developed by comparing 21 machine \r\nlearning classification models with Optuna hyperparameter optimization and validated across six independent external \r\ncohorts. Additionally, the expression patterns of the core diagnostic genes were experimentally validated. \r\nLinear Discriminant Analysis (LDA), along with four other machine learning models (please specify these models), ranked as the top five performing models \r\nacross six cohorts, demonstrating superior performance with AUC values exceeding 0.99 and all other performance metrics surpassing 0.899. This study marks \r\nthe first utilization of four specific novel machine learning models (again, please specify these models) in CRC diagnosis. \r\nThe robust performance of the top models across multiple external validation sets underscores the reliability and generalizability \r\nof our diagnostic model. 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