Microbes affect each other's growth in multiple, often elusive, ways. The ensuing interdependencies form complex networks, believed to reflect taxonomic composition as well community-level functional properties and dynamics. elucidation of these networks is pursued by measuring pairwise interactions coculture experiments. However, the combinatorial complexity precludes an exhaustive experimental analysis interactions, even for moderately sized microbial communities. Here, we used a machine...

Abstract Microbes affect each other’s growth in multiple, often elusive ways. The ensuing interdependencies form complex networks, believed to influence taxonomic composition, as well community-level functional properties and dynamics. Elucidation of these networks is pursued by measuring pairwise interaction co-culture experiments. However, combinatorial complexity precludes the exhaustive experimental analysis interactions even for moderately sized microbial communities. Here, we use a...

Abstract Machine learning is helping the interpretation of biological complexity by enabling inference and classification cellular, organismal ecological phenotypes based on large datasets, e.g. from genomic, transcriptomic metagenomic analyses. A number available algorithms can help search these datasets to uncover patterns associated with specific traits, including disease-related attributes. While, in many instances, treating an algorithm as a black box sufficient, it interesting pursue...

Machine learning is revolutionizing biology by facilitating the prediction of outcomes from complex patterns found in massive data sets. Large biological sets, like those generated transcriptome or microbiome studies,measure many relevant components that interact vivo with one another modular ways.Identifying high-order interactions machine models use to make predictions would facilitate development hypotheses linking combinations measured outcome. By using structure random forests, a new...

Machine learning is helping the interpretation of biological complexity by enabling inference and classification cellular, organismal ecological phenotypes based on large datasets, e.g., from genomic, transcriptomic metagenomic analyses. A number available algorithms can help search these datasets to uncover patterns associated with specific traits, including disease-related attributes. While, in many instances, treating an algorithm as a black box sufficient, it interesting pursue enhanced...