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

ABSTRACT: 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 phenot

SUBMITTER: Herbst K 

PROVIDER: S-EPMC10991586 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature