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MetaPheno: A Critical Evaluation of Deep Learning and Machine Learning in Metagenome-Based Disease Prediction.

Methods. 2019 Mar 15;:

Authors: LaPierre N, Ju CJ, Zhou G, Wang W

Abstract
The human microbiome plays a number of critical roles, impacting almost every aspect of human health and well-being. Conditions in the microbiome have been linked to a num-ber of significant diseases. Additionally, revolutions in sequencing technology have led to a rapid increase in publicly-available sequencing data. Consequently, there have been grow-ing efforts to predict disease status from metagenomic sequencing data, with a proliferation of new approaches in the last few years. Some of these efforts have explored utilizing a powerful form of machine learning called deep learning, which has been applied successfully in several biological domains. Here, we review some of these methods and the algorithms that they are based on, with a particular focus on deep learning methods. We also per-form a deeper analysis of Type 2 Diabetes and obesity datasets that have eluded improved results, using a variety of machine learning and feature extraction methods. We conclude by offering perspectives on study design considerations that may impact results and future directions the field can take to improve results and offer more valuable conclusions. The scripts and extracted features for the analyses conducted in this paper are available via GitHub: https://github.com/nlapier2/metapheno.

PMID: 30885720 [PubMed – as supplied by publisher]

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