[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128194-en":3,"doc-seo-128194-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128194,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes - Original Research","Ulcerative colitis (UC) is a chronic inflammatory bowel disease associated with immune dysregulation and increasing clinical burden. This study identifies immune-related biomarkers for UC and explores therapeutic targets by integrating external gene-expression datasets, differential gene analysis, WGCNA module screening, and ImmPort-derived immune-related genes. A diagnostic model is built using 113 combinations of 12 machine learning algorithms with cross-validation and external testing. Single-cell profiling, q-PCR verification, functional enrichment, immune infiltration analysis, and DSigDB drug screening with molecular docking and dynamics support candidate pathways and compounds for future UC drug development.","OPEN ACCESS  \nEDITED BY  \nChunying Li,  \nGeorgia State University, United States  \nREVIEWED BY  \nDuolong Zhu,  \nBaylor College of Medicine, United States Yuning Hou,  \nEmory University, United States  \n*CORRESPONDENCE  \nZhaoliang Ding  \n [18363325191@163.com](18363325191@163.com)  \n†These authors have contributed equally to this work  \nRECEIVED 05 February 2025  \nACCEPTED 28 May 2025  \nPUBLISHED 13 June 2025  \nCITATION  \nAn N, Lu Z, Li Y, Yang B, Ji S, Dong X and Ding Z (2025) An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes.  \nFront. Med. 12:1571529.  \ndoi: 10.3389/fmed.2025.1571529  \nCOPYRIGHT  \n© 2025 An, Lu, Li, Yang, Ji, Dong and Ding. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nTYPE Original Research PUBLISHED 13 June 2025  \nDOI 10.3389/fmed.2025.1571529  \nAn integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes  \nNa An 1†, Zhongwen Lu 2†, Yang Li3†, Bing Yang4, Shaozhen Ji3, Xu Dong 1 and Zhaoliang Ding4*  \n1Shandong University of Traditional Chinese Medicine, Jinan, China, 2The Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing, China, 3Zibo City Fourth People’s Hospital, Zibo, China, 4 Rizhao Hospital of Traditional Chinese Medicine, Rizhao, China  \nUlcerative colitis (UC) is a long-lasting inflammatory bowel disease that causes inflammation in the intestines and triggers autoimmune responses. This study aims to identify immune-related biomarkers for ulcerative colitis (UC) and explore potential therapeutic targets. First, we downloaded the expression profiles of datasets GSE87466, GSE87473, and GSE92415 from the GEO database. Next, we identified differentially expressed genes (DEGs) that are associated with UC. Using the WGCNA algorithm, we screened key module genes in UC and retrieved immune-related genes (IRGs) from the ImmPort database. We identified immune-related differentially expressed genes by intersecting the results from WGCNA, DEGs, and IRGs. To build a diagnostic model for UC, we applied 113 combinations of 12 machine learning algorithms. This included 10-fold crossvalidation on the training set and external validation on the test set. The single-cell results presented the cellular profile of UC and indicated that the key genes were significantly associated with macrophages, epithelial cells, and fibroblasts. The single-cell results presented the cell atlas of UC and suggested that key genes were significantly associated with macrophages, epithelial cells and fibroblasts. Quantitative polymerase chain reaction (q-PCR) was used to verify the expression levels of the core biomarkers screened out by machine learning. We conducted enrichment analysis using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA), which showed biological processes and signaling pathways associated with UC. Immune cell  \ninfiltration analysis based on CIBERSORT was also performed. We also screened potential drugs from the DSigDB drug database. To evaluate their effectiveness, we performed molecular docking and dynamics simulations. The results suggested that compounds like thalidomide and troglitazone are promising candidates for new UC drug development. Our findings provide insights into the pathogenesis of UC, its clinical treatment, and potential drug development.  \nKEYWORDS  \nulcerative colitis, machine learning, immunity, molecular docking, dynamics, single cell, quantit","cbCaid4hgueZJ7aX","https://ap.wps.com/l/cbCaid4hgueZJ7aX","pdf",8298609,2,1,21,"English","en",105,"# Introduction\n## Disease burden and extraintestinal complications\n## Limitations of current therapies","[{\"question\":\"What is the primary goal of the study on ulcerative colitis?\",\"answer\":\"To identify immune-related biomarkers for ulcerative colitis and explore potential therapeutic targets, including candidate drugs.\"},{\"question\":\"How did the researchers construct the UC diagnostic model?\",\"answer\":\"They combined key genes from differential expression, WGCNA module genes, and immune-related genes, then tested 113 combinations of 12 machine learning algorithms with cross-validation and external validation.\"},{\"question\":\"What experimental and computational steps were used to support the findings?\",\"answer\":\"Single-cell analyses were used to map gene associations with cell types, q-PCR verified core biomarkers, enrichment analyses assessed pathways, CIBERSORT estimated immune infiltration, and DSigDB screening followed by molecular docking and dynamics evaluated drug candidates.\"}]","An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes - Original Research | PDF",1785945450,53,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"an-integrated-machine-learning-framework-for-developing-and-validating-diagnostic-models-and-drug-predictions-based-on-ulcerative-colitis-genes-original-research","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/an-integrated-machine-learning-framework-for-developing-and-validating-diagnostic-models-and-drug-predictions-based-on-ulcerative-colitis-genes-original-research/128194/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the primary goal of the study on ulcerative colitis?","Question",{"text":76,"@type":77},"To identify immune-related biomarkers for ulcerative colitis and explore potential therapeutic targets, including candidate drugs.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How did the researchers construct the UC diagnostic model?",{"text":81,"@type":77},"They combined key genes from differential expression, WGCNA module genes, and immune-related genes, then tested 113 combinations of 12 machine learning algorithms with cross-validation and external validation.",{"name":83,"@type":74,"acceptedAnswer":84},"What experimental and computational steps were used to support the findings?",{"text":85,"@type":77},"Single-cell analyses were used to map gene associations with cell types, q-PCR verified core biomarkers, enrichment analyses assessed pathways, CIBERSORT estimated immune infiltration, and DSigDB screening followed by molecular docking and dynamics evaluated drug candidates.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]