Publication details.

Paper

Year:2022
Author(s):M.M. David, C. Tataru, Q. Pope, L.J. Baker, M.K. English, H.A. Epstein, A. Hammer, M. Kent, M.J. Sieler, R.S. Mueller, T.J. Sharpton, F. Tomas, R. Vega Thurber, X.Z. Fern
Title:Revealing General Patterns of Microbiomes That Transcend Systems: Potential and Challenges of Deep Transfer Learning
Journal:mSystems
ISSN:2379-5077
Volume:7
Issue No.:1
Pages:1
D.O.I.:https://doi.org/10.1128/msystems.01058-21
Web:https://journals.asm.org/doi/10.1128/msystems.01058-21
Abstract:A growing body of research has established that the microbiome can mediate the dynamics and functional capacities of diverse biological systems. Yet, we understand little about what governs the response of these microbial communities to host or environmental changes. Most efforts to model microbiomes focus on defining the relationships between the microbiome, host, and environmental features within a specified study system and therefore fail to capture those that may be evident across multiple systems. In parallel with these developments in microbiome research, computer scientists have developed a variety of machine learning tools that can identify subtle, but informative, patterns from complex data. Here, we recommend using deep transfer learning to resolve microbiome patterns that transcend study systems. By leveraging diverse public data sets in an unsupervised way, such models can learn contextual relationships between features and build on those patterns to perform subsequent tasks (e.g., classification) within specific biological contexts.

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  • Fiona Tomas Nash
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  • Marine Ecology