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

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Τίτλος: Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations.
Συγγραφείς: Herbst K; Bioinformatics Program, Boston University, Boston, MA, USA.; Biological Design Center, Boston University, Boston, MA, USA., Wang T; Division of Systems Engineering, Boston University, Boston, MA, USA., Forchielli EJ; Biological Design Center, Boston University, Boston, MA, USA.; Department of Biology, Boston University, Boston, MA, USA., Thommes M; Biological Design Center, Boston University, Boston, MA, USA.; Department of Biomedical Engineering, Boston University, Boston, MA, USA., Paschalidis IC; Division of Systems Engineering, Boston University, Boston, MA, USA. yannisp@bu.edu.; Department of Biomedical Engineering, Boston University, Boston, MA, USA. yannisp@bu.edu.; Faculty of Computing and Data Science, Boston University, Boston, MA, USA. yannisp@bu.edu.; Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA. yannisp@bu.edu., Segrè D; Bioinformatics Program, Boston University, Boston, MA, USA. dsegre@bu.edu.; Biological Design Center, Boston University, Boston, MA, USA. dsegre@bu.edu.; Department of Biology, Boston University, Boston, MA, USA. dsegre@bu.edu.; Department of Biomedical Engineering, Boston University, Boston, MA, USA. dsegre@bu.edu.; Faculty of Computing and Data Science, Boston University, Boston, MA, USA. dsegre@bu.edu.
Πηγή: Communications biology [Commun Biol] 2024 Apr 03; Vol. 7 (1), pp. 407. Date of Electronic Publication: 2024 Apr 03.
Τύπος έκδοσης: Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Nature Publishing Group UK Country of Publication: England NLM ID: 101719179 Publication Model: Electronic Cited Medium: Internet ISSN: 2399-3642 (Electronic) Linking ISSN: 23993642 NLM ISO Abbreviation: Commun Biol Subsets: MEDLINE
Imprint Name(s): Original Publication: London, United Kingdom : Nature Publishing Group UK, [2018]-
Ιατρικοί όροι (MeSH): Algorithms*, Phenotype
Περίληψη: 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.
(© 2024. The Author(s).)
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Grant Information: UH2 AG064704 United States AG NIA NIH HHS
Entry Date(s): Date Created: 20240403 Date Completed: 20240405 Latest Revision: 20260411
Update Code: 20260411
PubMed Central ID: PMC10991586
DOI: 10.1038/s42003-024-06093-w
PMID: 38570615
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:2399-3642
DOI:10.1038/s42003-024-06093-w