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Learning representations of microbe–metabolite interactions
- Morton, James T;
- Aksenov, Alexander A;
- Nothias, Louis Felix;
- Foulds, James R;
- Quinn, Robert A;
- Badri, Michelle H;
- Swenson, Tami L;
- Van Goethem, Marc W;
- Northen, Trent R;
- Vazquez-Baeza, Yoshiki;
- Wang, Mingxun;
- Bokulich, Nicholas A;
- Watters, Aaron;
- Song, Se Jin;
- Bonneau, Richard;
- Dorrestein, Pieter C;
- Knight, Rob
- et al.
Published Web Location
https://doi.org/10.1038/s41592-019-0616-3Abstract
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.
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