Deep learning predicts microbial interactions from self-organized spatiotemporal patterns
Joon‐Yong Lee, Natalie Sadler, Robert G. Egbert, Christopher Anderton, Kirsten Hofmockel, Janet Jansson, Hyun‐Seob Song
Abstract
lacked the ability to degrade chitin. Consistent with our expectations, our model predicted context-dependent interactions across two substrates, i.e., degrader-cheater relationship on chitin polymers and competition on monomers. The combined use of the agent-based model and machine learning algorithm successfully demonstrates how to infer microbial interactions from spatially distributed data, presenting itself as a useful tool for the analysis of more complex microbial community interactions.
Topics & Concepts
Computer scienceComputational biologyChemistryArtificial intelligenceBiologyCell Image Analysis TechniquesBioinformatics and Genomic NetworksMicrobial Community Ecology and Physiology