An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40584713", "kind": "science-record", "revision": 3, "content_hash": "sha256:98f82b67c89c6cb55b847d64d0ae45a420b50c2804915c4027982ea8a5b0c74b", "created_at": "2026-11-01T20:30:40Z", "updated_at": "2026-11-01T22:14:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes.", "doi": "10.3389/fmed.2025.1571529", "pmid": "40584713", "authors": ["Na An", "Zhongwen Lu", "Yang Li", "Bing Yang", "Shaozhen Ji", "Xu Dong", "Zhaoliang Ding"], "journal": "Frontiers in medicine", "year": 2025, "abstract": "Ulcerative 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 cross-validation 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 infiltration 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.", "claims": [{"id": "claim-method", "statement": "In the 10-fold cross-validation process, a total of 12 machine learning algorithms were combined: LASSO, Ridge, Stepglm, XGBoost, Random Forest (RF), Elastic Net (Enet), Partial Least Squares Regression for Generalized Linear Models (plsRglm), Generalized Boosted Regression Modeling (GBM), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Generalized Linear Model Boosting (glmBoost), and Support Vector Machine (SVM), Through arrangement, 113 combinations are formed, and model development is carried out on the basis of 10-fold cross-validation, to determine the most powerful diagnostic model based on 38 intersecting genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40584713", "locator": "sections[20] \"Machine learning for diagnostic signature genes\" paragraph 2", "quote": "In the 10-fold cross-validation process, a total of 12 machine learning algorithms were combined: LASSO, Ridge, Stepglm, XGBoost, Random Forest (RF), Elastic Net (Enet), Partial Least Squares Regression for Generalized Linear Models (plsRglm), Generalized Boosted Regression Modeling (GBM), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Generalized Linear Model Boosting (glmBoost), and Support Vector Machine (SVM), Through arrangement, 113 combinations are formed, and model development is carried out on the basis of 10-fold cross-validation, to determine the most powerful diagnostic model based on 38 intersecting genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: the original dataset utilized in this study was obtained from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), with the specific accession number and detailed data processing methodology comprehensively described in the Methods section.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40584713", "locator": "sections[34] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: the original dataset utilized in this study was obtained from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), with the specific accession number and detailed data processing methodology comprehensively described in the Methods section."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The glmBoost + Enet [alpha = 0.6] model selected five diagnostic genes (CCL18, DUOX2, GREM1, LCN2, and TNC), with a training AUC of 0.989 and an external-validation AUC of 0.791.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40584713", "locator": "sections[6] \"Identify hub genes with diagnostic value through machine learning and develop a diagnostic model for UC\" paragraph 2", "quote": "The AUC value of the training set was 0.989 (Figure 5C), and the AUC value of the validation set GSE165512 was 0.791 (Figure 5D), both demonstrating good predictive rates."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The glmBoost + Enet [alpha = 0.6] model selected five diagnostic genes (CCL18, DUOX2, GREM1, LCN2, and TNC), with a training AUC of 0.989 and an external-validation AUC of 0.791.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40584713"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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