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

Saved in:
Bibliographic Details
Title: Predicting 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, United States., Petry E; Center for Emerging and Re-Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, United States., Gu O; Center for Emerging and Re-Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, United States., Griebel BT; Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, WA, United States.; Department of Chemical Engineering, University of Washington, Seattle, WA, United States., Rustad TR; Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, WA, United States., Sherman DR; Department of Microbiology, University of Washington, Seattle, WA, United States., Yang JH; Center for Emerging and Re-Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, United States.; Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers New Jersey Medical School, Newark, NJ, United States., Ma S; Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, WA, United States.; Department of Chemical Engineering, University of Washington, Seattle, WA, United States.; Department of Pediatrics, University of Washington, Seattle, WA, United States.; Pathobiology Graduate Program, Department of Global Health, University of Washington, Seattle, WA, United States.
Source: Frontiers in tuberculosis [Front Tuberc] 2025; Vol. 3. Date of Electronic Publication: 2025 Apr 02.
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Media S.A Country of Publication: Switzerland NLM ID: 9918697582306676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2813-7868 (Electronic) Linking ISSN: 28137868 NLM ISO Abbreviation: Front Tuberc Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2813-7868
DOI:10.3389/ftubr.2025.1500899