Microbiome-based disease prediction with multimodal variational information bottlenecks.

ABSTRACT: Scientific research is shedding light on the interaction of the gut microbiome with the human host and on its role in human health. Existing machine learning methods have shown great potential in discriminating healthy from diseased microbiome states. Most of them leverage shotgun metagenomic sequencing to extract gut microbial species-relative abundances or strain-level markers. Each of these gut microbial profiling modalities showed diagnostic potential when tested separately; however, no existing approach combines them in a single predictive framework. Here, we propose the Multimodal Variational Information Bottleneck (MVIB), a novel deep learning model capable of learning a joint representation of multiple heterogeneous data modalities. MVIB achieves competitive classification performa

SUBMITTER: Grazioli F 

PROVIDER: S-EPMC9022840 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature