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...
Paper Details
Title
Interpretable and accurate prediction models for metagenomics data
Published Date
Mar 1, 2020
Journal
Volume
9
Issue
3
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