Print Email Facebook Twitter Identifying biological markers in the gut microbiome associated with celiac disease using machine learning Title Identifying biological markers in the gut microbiome associated with celiac disease using machine learning Author Persianov, Petr (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Abeel, T.E.P.M.F. (mentor) van der Toorn, E.A. (mentor) Calderon Franco, D. (mentor) Höllt, T. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-06-29 Abstract Celiac disease is a genetic autoimmune disorder caused by a negative reaction to gluten associated with alterations in the gut microbiome. This study explored the potential of machine learning models and feature selection methods in identifying biomarkers for celiac disease using gut microbiome data. The performance of several machine learning models was evaluated, and the impact of different feature selection methods, including MRMR, ANOVA, and information gain, was examined. The findings revealed comparable performance among the models without feature selection. However, the choice of feature selection method had varying effects on model performance, with logistic regression and support vector machines being more sensitive than random forest and XGBoost models. Notably, several identified bacteria species, such as Bacteroides eggerthii, Parabacteroides johnsonii, Faecalibacterium prausnitzii, and Ruminococcus_D bicirculans, have been previously associated with celiac disease, reinforcing their potential as biomarkers. Subject Celiac diseaseMachine learningGut microbiome To reference this document use: http://resolver.tudelft.nl/uuid:2bb4b19a-04b5-45d9-aa87-4719a7b1a574 Part of collection Student theses Document type bachelor thesis Rights © 2023 Petr Persianov Files PDF RP_Report_30.pdf 434.69 KB Close viewer /islandora/object/uuid:2bb4b19a-04b5-45d9-aa87-4719a7b1a574/datastream/OBJ/view