TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction.

ABSTRACT:

Motivation

Research supports the potential use of microbiome as a predictor of some diseases. Motivated by the findings that microbiome data is complex in nature, and there is an inherent correlation due to hierarchical taxonomy of microbial Operational Taxonomic Units (OTUs), we propose a novel machine learning method incorporating a stratified approach to group OTUs into phylum clusters. Convolutional Neural Networks (CNNs) were used to train within each of the clusters individually. Further, through an ensemble learning approach, features obtained from each cluster were then concatenated to improve prediction accuracy. Our two-step approach comprising stratification prior to combining multiple CNNs, aided in capturing the relationships between OTUs sharing a phylum efficiently,

SUBMITTER: Sharma D 

PROVIDER: S-EPMC7750934 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature