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Multinomial modelling of TB/HIV co-infection yields a robust predictive signature and generates hypotheses about the HIV+TB+ disease state
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Fergal J. Duffy,
Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing
Affiliations Seattle Children’s Research Institute, Center for Global Infectious Disease Research, Seattle, WA, United States of America, Center for Infectious Disease Research (formerly Seattle Biomedical Research Institute), Seattle, WA, United States of America
⨯ - Ethan G. Thompson,
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Thomas J. Scriba,
Roles Conceptualization, Funding acquisition, Methodology, Supervision, Writing – review & editing
Affiliation South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine & Department of Pathology, University of Cape Town, Cape Town, South Africa
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Daniel E. Zak
Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Supervision, Writing – review & editing
* E-mail: danielzak000@gmail.com
Affiliation Center for Infectious Disease Research (formerly Seattle Biomedical Research Institute), Seattle, WA, United States of America
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http://orcid.org/0000-0002-4675-0937
Multinomial modelling of TB/HIV co-infection yields a robust predictive signature and generates hypotheses about the HIV+TB+ disease state
- Fergal J. Duffy,
- Ethan G. Thompson,
- Thomas J. Scriba,
- Daniel E. Zak
- Published: July 15, 2019
- https://doi.org/10.1371/journal.pone.0219322
- See the preprint