Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations

Herbst, Konrad; Wang, Taiyao; Forchielli, Elena J.; Thommes, Meghan; Paschalidis, Ioannis Ch.; Segrè, Daniel

doi:10.1038/s42003-024-06093-w

Title: Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations

Journal Article · 02 April 2024 · Communications Biology

DOI: https://doi.org/10.1038/s42003-024-06093-w · OSTI ID:2469647

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^[1]; Forchielli, Elena J. ^[1]; Thommes, Meghan ^[1];

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Boston University, MA (United States)

The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We apply the algorithm to three microbial datasets and identify environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: Human Frontiers Science Program; National Institutes of Health (NIH); National Science Foundation (NSF); US Department of the Navy, Office of Naval Research (ONR); USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2469647

Journal Information:: Communications Biology, Journal Name: Communications Biology Journal Issue: 1 Vol. 7; ISSN 2399-3642

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations

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