Hacilar, HilalNalbantoĝlu, Özkan UfukBakir-Güngör, Burcu2025-09-252025-09-2520189781538678930https://doi.org/10.1109/UBMK.2018.8566487https://hdl.handle.net/20.500.12573/4151Hacilar, Hilal/0000-0002-5811-6722; Bakir-Gungor, Burcu/0000-0002-2272-6270There is an ongoing interplay between humans and our microbial communities. The microorganisms living in our gut produce energy from our food, strengthen our immune system, break down foreign products, and release metabolites and hormones, which are significant for regulating our physiology. The shifts away from this 'healthy' gut microbiome is considered to be associated with many diseases. Inflammatory bowel diseases (IBD) including Crohn's disease and ulcerative colitis, are gut related disorders affecting the intestinal tract. Although some metagenomics studies are conducted on IBD recently, our current understanding of the precise relationships between the human gut microbiome and IBD remains limited. In this regard, the use of state-of-the art machine learning approaches became popular to address a variety of questions like early diagnosis of certain diseases using human microbiota. In this study, we investigate which subset of gut microbiota are mostly associated with IBD and if disease-associated biomarkers can be detected via applying state-of-the art machine learning algorithms and proper feature selection methods. © 2019 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessFeature SelectionHuman Gut MicrobiotaInflammatory Bowel Diseases (Ibd)Machine LearningMetagenomicsPredictionDiagnosisDiseasesFeature ExtractionForecastingLearning AlgorithmsLearning SystemsMicroorganismsFeature Selection MethodsHuman Gut MicrobiotaHuman MicrobiotaInflammatory Bowel DiseaseMachine Learning ApproachesMetagenomicsMicrobial CommunitiesUlcerative ColitisMachine LearningConference Object10.1109/UBMK.2018.85664872-s2.0-85060602166