Multivariable regression models improve accuracy and sensitive grading of antibiotic resistance mutations in Mycobacterium tuberculosis.
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| Title: | Multivariable regression models improve accuracy and sensitive grading of antibiotic resistance mutations in Mycobacterium tuberculosis. |
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| Authors: | Kulkarni SG; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA., Laurent S; Foundation for Innovative New Diagnostics (FIND), Geneva, Switzerland., Miotto P; IRCCS San Raffaele Scientific Institute, Milano, Italy., Walker TM; Nuffield Department of Medicine, University of Oxford, Oxford, UK.; Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam., Chindelevitch L; Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK., Nathanson CM; Global Tuberculosis Programme, World Health Organization (WHO), Geneva, Switzerland., Ismail N; Global Tuberculosis Programme, World Health Organization (WHO), Geneva, Switzerland.; Department of Clinical Microbiology and Infectious Diseases, University of the Witwatersrand, Johannesburg, South Africa., Rodwell TC; Foundation for Innovative New Diagnostics (FIND), Geneva, Switzerland.; Division of Pulmonary, Critical Care and Sleep Medicine, University of California, San Diego, CA, USA., Farhat MR; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. maha_farhat@hms.harvard.edu.; Division of Pulmonary & Critical Care, Massachusetts General Hospital, Boston, MA, USA. maha_farhat@hms.harvard.edu. |
| Source: | Nature communications [Nat Commun] 2025 Mar 04; Vol. 16 (1), pp. 2149. Date of Electronic Publication: 2025 Mar 04. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 2041-1723 |
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| DOI: | 10.1038/s41467-025-57174-1 |
