Learning representations of microbe–metabolite interactions View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-11-04

AUTHORS

James T. Morton, Alexander A. Aksenov, Louis Felix Nothias, James R. Foulds, Robert A. Quinn, Michelle H. Badri, Tami L. Swenson, Marc W. Van Goethem, Trent R. Northen, Yoshiki Vazquez-Baeza, Mingxun Wang, Nicholas A. Bokulich, Aaron Watters, Se Jin Song, Richard Bonneau, Pieter C. Dorrestein, Rob Knight

ABSTRACT

Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks (https://github.com/biocore/mmvec) to estimate the conditional probability that each molecule is present given the presence of a specific microorganism. We show with known environmental (desert soil biocrust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe–metabolite relationships, and demonstrate how the method can discover relationships between microbially produced metabolites and inflammatory bowel disease. More... »

PAGES

1306-1314

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41592-019-0616-3

    DOI

    http://dx.doi.org/10.1038/s41592-019-0616-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1122295299

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/31686038


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