[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118996-en":3,"doc-seo-118996-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118996,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",7,"Healthcare","Colorectal Cancer Detection via Metabolites and Machine Learning","Colorectal cancer (CRC) diagnosis currently relies on colonoscopy, an effective but invasive screening method that carries patient risk. Metabolomics offers a non-invasive alternative by using identified biomarkers to detect cancer-associated metabolic signatures. This study develops machine-learning models based on chemical descriptors to recognize CRC-associated metabolites. Selected biomarker metabolites were used to train multiple models, and the best models achieved 89.55% accuracy for Stage 0–2, 95.21% for Stage 3–4, and 93.04% for Stage 0–4. Models were validated on independent datasets, including random and unrelated-disease metabolites.","UC San Diego  \nUC San Diego Previously Published Works  \nTitle  \nColorectal Cancer Detection via Metabolites and Machine Learning.  \nPermalink  \n[https://escholarship.org/uc/item/31s005gs](https://escholarship.org/uc/item/31s005gs)  \nJournal  \nCurrent Issues in Molecular Biology, 46(5)  \nAuthors  \nYang, Rachel Tsigelny, Igor Kesari, Santoshet al.  \nPublication Date  \n2024-04-30  \nDOI  \n10.3390/cimb46050254  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nArticle  \nColorectal Cancer Detection via Metabolites and Machine Learning  \nRachel Yang 1, Igor F. Tsigelny 2,3,4,5,*, Santosh Kesari 6 and Valentina L. Kouznetsova 2,3,5  \nCitation: Yang, R.; Tsigelny, I.F.; Kesari, S.; Kouznetsova, V.L. Colorectal Cancer Detection via Metabolites and Machine Learning. Curr. Issues Mol. Biol. 2024, 46, 4133–4146. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)cimb46050254  \nAcademic Editors: Irina  \nV. Kondakova, Liudmila V. Spirina, Natalya V. Yunusova and Quan Zou  \nReceived: 21 March 2024  \nRevised: 23 April 2024  \nAccepted: 24 April 2024  \nPublished: 30 April 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 REHS Program, San Diego Supercomputer Center, University of California San Diego, MC 0505, 9500 Gilman Drive, La Jolla, CA 92093, USA  \n2 San Diego Supercomputer Center, University of California San Diego, MC 0505, 9500 Gilman Drive, La Jolla, CA 92093, USA; [vkouznetsova@ucsd.edu](vkouznetsova@ucsd.edu)  \n[3](3 BiAna)[ BiAna](3 BiAna), [P.O](P.O). Box 2525, La Jolla, CA 92038, USA  \n4 Department of Neurosciences, University of California San Diego, MC00505, 9500 Gilman Drive, La Jolla, CA 92093, USA  \n5 CureScience Institute, 5820 Oberlin Drive, STE 202, San Diego, CA 92121, USA  \n6 Pacific Neuroscience Institute, 2125 Arizona Avenue, Santa Monica, CA 90404, USA; [santosh.kesari@providence.org](santosh.kesari@providence.org)  \n* Correspondence: igor@curescience.org or [itsigeln@ucsd.edu](itsigeln@ucsd.edu)  \nAbstract: Today, colorectal cancer (CRC) diagnosis is performed using colonoscopy, which is the current, most effective screening method. However, colonoscopy poses risks of harm to the patient and is an invasive process. Recent research has proven metabolomics as a potential, non-invasive detection method, which can use identified biomarkers to detect potential cancer in a patient’s body. The aim of this study is to develop a machine-learning (ML) model based on chemical descriptors that will recognize CRC-associated metabolites. We selected a set of metabolites found as the biomarkers of CRC, confirmed that they participate in cancer-related pathways, and used them for training a machine-learning model for the diagnostics of CRC. Using a set of selective metabolites and random compounds, we developed a range of ML models. The best performing ML model trained on Stage 0–2 CRC metabolite data predicted a metabolite class with 89.55% accuracy. The best performing ML model trained on Stage 3–4 CRC metabolite data predicted a metabolite class with 95.21% accuracy. Lastly, the best-performing ML model trained on Stage 0–4 CRC metabolite data predicted a metabolite class with 93.04% accuracy. These models were then tested on independent datasets, including random and unrelated-disease metabolites. In addition, six pathways related to these CRC metabolites were also distinguished: aminoacyl-tRNA biosynthesis; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and alanine, aspartate, and glutamate metaboli","cbCaimUgdfkVCnm6","https://ap.wps.com/l/cbCaimUgdfkVCnm6","pdf",7395764,1,15,"English","en",105,"# Abstract\n# Introduction\n## Current CRC screening and limitations\n## Metabolomics as a non-invasive detection approach","[{\"question\":\"Why is colonoscopy not ideal for colorectal cancer screening?\",\"answer\":\"Colonoscopy is invasive and can cause risks such as harm to the patient, sedation-related concerns, and complications including bleeding or tearing. The study highlights the need for safer early detection methods.\"},{\"question\":\"What is the main goal of this study?\",\"answer\":\"The study aims to develop machine-learning models that recognize CRC-associated metabolites using chemical descriptors, supporting non-invasive diagnosis.\"},{\"question\":\"How accurate are the best machine-learning models for different CRC stages?\",\"answer\":\"The best model for Stage 0–2 predicted a metabolite class with 89.55% accuracy, Stage 3–4 with 95.21% accuracy, and Stage 0–4 with 93.04% accuracy.\"}]","Colorectal Cancer Detection via Metabolites and Machine Learning | PDF",1785721604,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"colorectal-cancer-detection-via-metabolites-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/colorectal-cancer-detection-via-metabolites-and-machine-learning/118996/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is colonoscopy not ideal for colorectal cancer screening?","Question",{"text":75,"@type":76},"Colonoscopy is invasive and can cause risks such as harm to the patient, sedation-related concerns, and complications including bleeding or tearing. The study highlights the need for safer early detection methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the main goal of this study?",{"text":80,"@type":76},"The study aims to develop machine-learning models that recognize CRC-associated metabolites using chemical descriptors, supporting non-invasive diagnosis.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate are the best machine-learning models for different CRC stages?",{"text":84,"@type":76},"The best model for Stage 0–2 predicted a metabolite class with 89.55% accuracy, Stage 3–4 with 95.21% accuracy, and Stage 0–4 with 93.04% accuracy.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]