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

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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
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PubType: Academic Journal
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  Data: Predicting fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning.
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  Data: <searchLink fieldCode="AU" term="%22Bustad+E%22">Bustad E</searchLink>; Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, WA, United States.<br /><searchLink fieldCode="AU" term="%22Petry+E%22">Petry E</searchLink>; Center for Emerging and Re-Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, United States.<br /><searchLink fieldCode="AU" term="%22Gu+O%22">Gu O</searchLink>; Center for Emerging and Re-Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, United States.<br /><searchLink fieldCode="AU" term="%22Griebel+BT%22">Griebel BT</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Rustad+TR%22">Rustad TR</searchLink>; Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, WA, United States.<br /><searchLink fieldCode="AU" term="%22Sherman+DR%22">Sherman DR</searchLink>; Department of Microbiology, University of Washington, Seattle, WA, United States.<br /><searchLink fieldCode="AU" term="%22Yang+JH%22">Yang JH</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Ma+S%22">Ma S</searchLink>; 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.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Frontiers+Media+S%2EA%22">Frontiers Media S.A </searchLink><i>Country of Publication: </i>Switzerland <i>NLM ID: </i>9918697582306676 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2813-7868 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2228137868%22">28137868 </searchLink><i>NLM ISO Abbreviation: </i>Front Tuberc <i>Subsets: </i>PubMed not MEDLINE
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        Value: 10.3389/ftubr.2025.1500899
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        Text: English
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      – TitleFull: Predicting fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning.
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              Text: 2025
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              Y: 2025
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