Deep Neural Network Modeling for Phenotypic Prediction of Metagenomic Samples

Yassin Mreyoud, Tae-Hyuk Ahn

Abstract: The increasing popularity of metagenomic sequencing has resulted in a plethora of 16S RNA and whole genome sequence data available. Microbes play an important role in the health and disease of humans, pets, and livestock. Characterizing such microbes and their relative abundances are important to identify sample phenotypes such as disease. In the past, machine learning based methods have been applied for prediction of host disease status and overall health based on taxonomic abundance profiles. Here we utilize deep neural network modeling with taxonomic profiles for faster, precise, and effective prediction of metagenomic sample phenotypes.