Predicting fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning
These integrative network modeling and machine learning analyses enable the prediction of mycobacterial fitness across different environmental and genetic contexts with mechanistic detail. We envision these models can inform the future design of prognostic assays and therapeutic interventions that can cripple Mtb growth and survival to cure tuberculosis disease.
Authors
- Tige R. Rustad (ORCID: https://orcid.org/0000-0003-2996-2943)
- Shuyi Ma (ORCID: https://orcid.org/0000-0003-0783-1328)
- Jason H. Yang (ORCID: https://orcid.org/0000-0003-0921-4657)
- David R. Sherman (ORCID: https://orcid.org/0000-0003-2185-944X)
- Ethan Bustad (ORCID: https://orcid.org/0000-0002-0230-2913)
- Braden T Griebel (ORCID: https://orcid.org/0009-0003-3617-5734)
- Edson Petry
- Oliver Gu
Institutions
- Rutgers, The State University of New Jersey (US)
- Infectious Disease Research Institute (US)
- University of Washington (US)
- Rutgers New Jersey Medical School (US)
Publication Details
- Journal
- Frontiers in Tuberculosis
- Published
- 2025-04-02
- DOI
- https://doi.org/10.3389/ftubr.2025.1500899
- Citations
- 2
- Primary Topic
- Tuberculosis Research and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 2.79
Funders
- Agilent Technologies
- Seattle Children's Research Institute
- Children’s Hospital of Wisconsin Research Institute
- National Institutes of Health