Author

J. E. Posey

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Microbiology· 2

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Assay:protein structure· 1 Research Focus:variant effect prediction· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

A. Mercante· 1 shared A. S. Walker· 1 shared B. Metchock· 1 shared C. I. Lynch· 1 shared D. Adlard· 1 shared D. W. Crook· 1 shared G. P. Morlock· 1 shared J. Carter· 1 shared

Activity by Year

2018-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperMicrobiology

A. Mercante

Mutual co-authors in cluster

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2 papers

Recent·Most cited

Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine learning approaches.

J. Carter, T. M. Walker, A. S. Walker, M. Whitfield, G. P. Morlock, C. I. Lynch, D. Adlard, T. E. Peto, J. E. Posey, D. W. Crook, P. W. Fowler

This paper predicts pyrazinamide resistance in Mycobacterium tuberculosis using machine learning models trained on pncA mutations and structural features, achieving 80.2% sensitivity and 76.9% specificity.

bioRxiv · Nov 14, 2023 · PreprintMicrobiology

Validation of novel Mycobacterium tuberculosis isoniazid resistance mutations not detectable by common molecular tests

J. L. Kandler, A. Mercante, T. Dalton, M. N. Ezewudo, L. Cowan, S. P. Burns, B. Metchock, P. Cegielski, J. E. Posey

The paper identifies novel isoniazid resistance mutations in Mycobacterium tuberculosis using whole genome sequencing and functional validation, showing WGS improves detection of resistance beyond common tests.

bioRxiv · May 15, 2018Microbiology