Wasim, AyeshaMartin-De Arribas, E.Ladra, SusanaPoza, MargaritaFernández-Lozano, Carlos2026-09-012026-09-012024A. Wasim, E. Martín-de Arribas, S. Ladra, M. Poza, and C. Fernández-Lozano, "Machine Learning Decodes the Microbiome: Advancing Diagnostic Accuracy in Gastrointestinal Diseases" [poster], 11th ISM World Congress on Targeting Microbiota, 14-15 October 2024, Maltahttps://hdl.handle.net/2183/49122Presentado en: 11th ISM World Congress on Targeting Microbiota, 14-15 October 2024, Malta Versión aceptada[Abstract]: This study evaluates the diagnostic accuracy of machine learning (ML) models on gut microbiome data for gastrointestinal diseases. By assessing batch correction and profiling strategies, the results show that species-level taxonomic features excel for Crohn's disease, whereas ComBat-seq batch correction improves predictive accuracy in colorectal cancer. Algorithms like Random Forest and XGBoost proved most effective, offering guidance for reproducible non-invasive diagnostics.eng© 2024Machine learningGut microbiomeGastrointestinal diseasesconference outputopen access