Use of machine learning to diagnose inflammatory bowel disease using associated metagenomics dataset
Creators: name: Bakir-Gungor, Burcu
name: Hacilar, Hilal
name: Ozkan Ufuk Nalbantoglu
name: Aran, Oya
Publisher: name: F1000 Research Limited
Year: 2019

description: Motivation:<br>Human gut microbiota is a complex community of microorganisms including trillions of bacteria that populate our gastrointestinal tract. While some of these microorganisms are considered as essential regulators of our immune system, some others can cause several diseases such as Inflammatory bowel diseases (IBD), diabetes, and cancer. In recent decades, the rapid advances in next generation sequencing technologies accelerate the discovery of the human microbiome. IBD, comprising Crohn’s disease and ulcerative colitis, is a gut related disorder where the deviations from the “healthy” gut microbiome is considered to be associated with IBD. Since the aetiology of IBD is not fully understood and its symptoms are complex, the design of new tools that make use of the available human gut metagenome data is essential for the diagnosis of IBD. In this respect, machine learning is well suited to obtain a diagnostic model using IBD-associated metagenomics dataset.<br><br>Method:<br>In this study, we aim to develop a classification model to aid IBD diagnosis and to discover IBD-associated biomarkers using metagenomics data. In this regard, the sequencing data of 148 IBD patients and 234 healthy individuals were fetched from MetaHit project and categorized into disease states based on the associated metadata. Sequencing reads were assigned to taxa using MetaPhlAn2 taxonomic classification tool. To deal with the high dimensionality of features, we applied robust feature selection algorithms such as Conditional Mutual Information Maximization (CMIM), Fast Correlation Based Filter (FCBF), min redundancy max relevance (mRMR) and Extreme Gradient Boosting (XGBoost). We compared the performances of machine learning methods such as support vector machines, k-nearest neighbor (kNN), random forest, adaboost, logitboost, decision tree and some hybrid methods. Additionally, we attempted to understand the underlying structure of IBD metagenomics data using Principal Compenent Analysis (PCA), and to find the subgroups of IBD patients using k-means and hierarchical clustering.<br><br>Results:<br>1455 taxa were identified from the IBD metagenomics dataset and used to train and test our model. In our experiments with 10-fold cross validation, we observed that XGBoost has a considerable effect in terms of minimizing the microbiota used for the diagnosis of IBD and thus reducing the cost and time. When we analyzed these identified species, we found that most of them are known as related with IBD development mechanisms<i>.</i> We also observed that compared to the single classifiers, ensemble methods such as kNN+logitboost resulted in better performance measures (95,6% AUC, 89% F1-score, 91,623% accuracy) for the classification of IBD.<br><br>Conclusions:<br>Metagenomic analysis of human microbiome reveals significant phenotypical signals, such as disease, as microbiome is modulated via human-microbiome symbiosis. Since the accuracy of diagnosis in IBD is key for a prompt and effective treatment, there is an utmost need to develop a classification technique which can expedite IBD diagnosis. In this respect, this study utilizes several supervised and unsupervised machine learning algorithms to increase the diagnostic accuracy of IBD, investigates potential pathobionts of IBD, and finds out which subset of microbiota is more informative than other taxa applying some of the state-of-the art feature selection methods.