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

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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
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DbLabel: MEDLINE Ultimate
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PubType: Academic Journal
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  Data: Predicting bacterial 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, USA.<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, USA.<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, USA.<br /><searchLink fieldCode="AU" term="%22Griebel+BT%22">Griebel BT</searchLink>; Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle WA, USA.; Department of Chemical Engineering, University of Washington, Seattle WA, USA.<br /><searchLink fieldCode="AU" term="%22Rustad+TR%22">Rustad TR</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Sherman+DR%22">Sherman DR</searchLink>; Department of Microbiology, University of Washington, Seattle WA, USA.<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, USA.; Department of Microbiology, Biochemistry, & Molecular Genetics, Rutgers New Jersey Medical School, Newark NJ, USA.<br /><searchLink fieldCode="AU" term="%22Ma+S%22">Ma S</searchLink>; 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.
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  Data: <searchLink fieldCode="JN" term="%22101680187%22">BioRxiv : the preprint server for biology</searchLink> [bioRxiv] 2024 Sep 25. <i>Date of Electronic Publication: </i>2024 Sep 25.
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  Data: <i>Country of Publication: </i>United States <i>NLM ID: </i>101680187 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2692-8205 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2226928205%22">26928205 </searchLink><i>NLM ISO Abbreviation: </i>bioRxiv <i>Subsets: </i>PubMed not MEDLINE
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        Value: 10.1101/2024.09.23.614645
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        Text: English
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            – D: 25
              M: 09
              Text: 2024 Sep 25
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