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The tremendous boost in the next generation sequencing and in the omicstechnologies makes it possible to characterize human gut microbiome (thecollective genomes of the microbial community that reside in ourgastrointestinal tract). While some of these microorganisms are considered asessential regulators of our immune system, some others can cause severaldiseases such as Inflammatory Bowel Diseases (IBD), diabetes, and cancer. IBD,is a gut related disorder where the deviations from the healthy gut microbiomeare considered to be associated with IBD. Although existing studies attempt tounveal the composition of the gut microbiome in relation to IBD diseases, acomprehensive picture is far from being complete. Due to the complexity ofmetagenomic studies, the applications of the state of the art machine learningtechniques became popular to address a wide range of questions in the field ofmetagenomic data analysis. In this regard, using IBD associated metagenomicsdataset, this study utilizes both supervised and unsupervised machine learningalgorithms, i) to generate a classification model that aids IBD diagnosis, ii)to discover IBD associated biomarkers, iii) to find subgroups of IBD patientsusing k means and hierarchical clustering. To deal with the high dimensionalityof features, we applied robust feature selection algorithms such as ConditionalMutual Information Maximization (CMIM), Fast Correlation Based Filter (FCBF),min redundancy max relevance (mRMR) and Extreme Gradient Boosting (XGBoost). Inour experiments with 10 fold cross validation, XGBoost had a considerableeffect in terms of minimizing the microbiota used for the diagnosis of IBD andthus reducing the cost and time. We observed that compared to the singleclassifiers, ensemble methods such as kNN and logitboost resulted in betterperformance measures for the classification of IBD.

Inflammatory Bowel Disease Biomarkers of Human Gut Microbiota Selected via Ensemble Feature Selection Methods