The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Learning representations of microbe–metabolite interactions
Open accessAbstract
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. mmvec, a neural-network-based algorithm, uses paired multiomics data (microbial sequence counts and metabolite abundances) to compute the conditional probability of observing a metabolite in the presence of a specific microorganism.
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Metadata
- Authors
- Morton, J. T., Aksenov, A. A., Nothias, L. F., Foulds, J. R., Quinn, R. A., Badri, M. H., Swenson, T. L., Van Goethem, M. W., Northen, T. R., Vazquez-Baeza, Y., Wang, M., Bokulich, N. A., Watters, A., Song, S. J., Bonneau, R., Dorrestein, P. C., & Knight, R.
- Venue
- Nature Methods
- Published
- 2019-11-04
- Volume
- 16
- Issue
- 12
- Pages
- 1306-1314
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2987653794
- Cited by
- 299
- References
- 58
- FWCI
- 9.60
- Type
- journal article
- Language
- en
- Open access
- green
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- Morton, J. T., Aksenov, A. A., Nothias, L. F., Foulds, J. R., Quinn, R. A., Badri, M. H., Swenson, T. L., Van Goethem, M. W., Northen, T. R., Vazquez-Baeza, Y., Wang, M., Bokulich, N. A., Watters, A., Song, S. J., Bonneau, R., Dorrestein, P. C., & Knight, R. (2019). Learning representations of microbe–metabolite interactions. Nature Methods. https://doi.org/10.1038/s41592-019-0616-3
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