A Complementary Approach in the Analysis of the Human Gut Microbiome Applying Self-organizing Maps and Random Forest

BURGOS, VALERIA; PIÑERO, TAMARA A.; FERNÁNDEZ, MARÍA LAURA

Buenos Aires

Conferencia; ICAI 2021 Fourth International Conference on Applied Informatics; 2021

Universidad Tecnológica Nacional Facultad Regional Buenos Aires

Abstract. The human gastrointestinal tract is colonized by millions of microorganisms that make up the so-called gut microbiota, with a vital role in the well-being, health maintenance as well as the appearance of several diseases in the human host. A data mining analysis approach was applied on a set of gut microbiota data from healthy individuals. We used two machine learning methods to identify biomedically relevant relationships between demographic and biomedical variables of the subjects and patterns of abundance of bacteria. The study was carried out focusing on the two most abundant human gut microbiota groups, Bacteroidetes and Firmicutes. Both subsets of bacterial abundances together with the metadata variables were subjected to an exploratory analysis, using self-organizing maps that integrate multivariate information through different component planes. Finally, to evaluate the relevance of the variables on the biological diversity of the microbial communities, an ensemble-based method such as random forest was used. Results showed that age and body mass index were among the most important features at explaining bacteria diversity. Interestingly, several bacteria species known to be associated to diet and obesity were identified as relevant features as well. In the topological analysis of self-organizing maps, we identified certain groups of nodes with similarities in subject metadata and gut bacteria. We conclude that our results represent a preliminary approach that could be considered, in future studies, as a potential complement in health reports so as to help health professionals personalize patient treatment or support decision making. Keywords: Gut microbiome · Self-organizing maps · Random forest · Precision medicine · Bioinformatics