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Scalable learning of interpretable rules for the dynamic microbiome domain
The microbiome, which is inherently dynamic, plays essential roles in human physiology and its disruption has been implicated in numerous human diseases. Linking dynamic changes in the microbiome to the status of the human host is an important problem, which is complicated by limitations and complexities of the data. Model interpretability is key in the microbiome field, as practitioners seek to derive testable biological hypotheses from data or develop diagnostic tests that can be understooddoi:10.1101/2020.06.25.172270 fatcat:5oxpacwayzhbzlceoc2zwnv3ta