Interpretable and accurate prediction models for metagenomics data.

ABSTRACT:

Background

Microbiome biomarker discovery for patient diagnosis, prognosis, and risk evaluation is attracting broad interest. Selected groups of microbial features provide signatures that characterize host disease states such as cancer or cardio-metabolic diseases. Yet, the current predictive models stemming from machine learning still behave as black boxes and seldom generalize well. Their interpretation is challenging for physicians and biologists, which makes them difficult to trust and use routinely in the physician-patient decision-making process. Novel methods that provide interpretability and biological insight are needed. Here, we introduce "predomics", an original machine learning approach inspired by microbial ecosystem interactions that is tailored for metagenomics data.

SUBMITTER: Prifti E 

PROVIDER: S-EPMC7062144 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature