Predicting bacterial fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning.

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Bibliographic Details
Title: Predicting bacterial fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning.
Authors: Bustad E; Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle WA, USA., Petry E; Center for Emerging and Re-emerging Pathogens, Rutgers New Jersey Medical School, Newark NJ, USA., Gu O; Center for Emerging and Re-emerging Pathogens, Rutgers New Jersey Medical School, Newark NJ, USA., Griebel BT; Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle WA, USA.; Department of Chemical Engineering, University of Washington, Seattle WA, USA., Rustad TR; Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle WA, USA.; Center for Emerging and Re-emerging Pathogens, Rutgers New Jersey Medical School, Newark NJ, USA.; Department of Chemical Engineering, University of Washington, Seattle WA, USA.; Department of Microbiology, University of Washington, Seattle WA, USA.; Department of Microbiology, Biochemistry, & Molecular Genetics, Rutgers New Jersey Medical School, Newark NJ, USA.; Department of Pediatrics, University of Washington, Seattle WA, USA.; Pathobiology Graduate Program, Department of Global Health, University of Washington, Seattle WA, USA., Sherman DR; Department of Microbiology, University of Washington, Seattle WA, USA., Yang JH; Center for Emerging and Re-emerging Pathogens, Rutgers New Jersey Medical School, Newark NJ, USA.; Department of Microbiology, Biochemistry, & Molecular Genetics, Rutgers New Jersey Medical School, Newark NJ, USA., Ma S; Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle WA, USA.; Department of Chemical Engineering, University of Washington, Seattle WA, USA.; Department of Pediatrics, University of Washington, Seattle WA, USA.; Pathobiology Graduate Program, Department of Global Health, University of Washington, Seattle WA, USA.
Source: BioRxiv : the preprint server for biology [bioRxiv] 2024 Sep 25. Date of Electronic Publication: 2024 Sep 25.
Publication Type: Journal Article; Preprint
Journal Info: Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet ISSN: 2692-8205 (Electronic) Linking ISSN: 26928205 NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2692-8205
DOI:10.1101/2024.09.23.614645