Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks
Abstract
Different organisms in a microbial community may drastically affect each other’s growth phenotypes, significantly affecting the community dynamics, with important implications for human and environmental health. Novel culturing methods and the decreasing costs of sequencing will gradually enable high-throughput measurements of pairwise interactions in systematic coculturing studies. However, a thorough characterization of all interactions that occur within a microbial community is greatly limited both by the combinatorial complexity of possible assortments and by the limited biological insight that interaction measurements typically provide without laborious specific follow-ups. Here, we show how a simple and flexible formal representation of microbial pairs can be used for the classification of interactions via machine learning. The approach we propose predicts with high accuracy the outcome of yet-to-be performed experiments and generates testable hypotheses about the mechanisms of specific interactions.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 30, 2018
- Source ID
- 10.1128/msystems.00181-18
Entities
People
- Daniel Segrè
- Demetrius Dimucci
- Mark Kon
Organizations
- Boston University
- Human Frontier Science Program
- National Institute of Dental and Craniofacial Research
- National Institute of General Medical Sciences
- National Science Foundation
- United States Department of Energy