Author

J. Larkins-Ford

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· 4

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 4 Approach:experimental assay· 2 Approach:machine learning (classical)· 2 Organism:bacteria (other)· 1 Research Focus:cell signaling & trafficking· 1 Research Focus:cell type annotation· 1 Organism:human· 1 Assay:imaging· 1

Frequent Co-Authors

B. B. Aldridge· 3 shared N. Van· 3 shared Y. N. Degefu· 3 shared A. Sokolov· 2 shared M. C. Olson· 2 shared T. C. Smith· 2 shared B. Aldridge· 1 shared C. M. Thorpe· 1 shared

Activity by Year

2020-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 28 mutual co-author links

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3 shared papersMicrobiology

B. B. Aldridge

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Author profile →

4 papers

Recent·Most cited

Novel synergies and isolate specificities in the drug interactions landscape of Mycobacterium abscessus

N. Van, Y. N. Degefu, P. A. Leus, J. Larkins-Ford, J. Klickstein, F. P. Maurer, D. Stone, H. Poonawala, C. M. Thorpe, T. C. Smith, B. B. Aldridge

This study systematically measures drug interactions in Mycobacterium abscessus to identify synergistic combinations and highlight strain-specific responses.

bioRxiv · Dec 12, 2022Microbiology

Design principles to assemble drug combinations for effective tuberculosis therapy using interpretable pairwise drug response measurements

J. Larkins-Ford, Y. N. Degefu, N. Van, A. Sokolov, B. B. Aldridge

The paper uses pairwise drug response data to train machine learning models predicting effective tuberculosis drug combinations, revealing design principles for rational combination therapy.

bioRxiv · Dec 5, 2021Microbiology

Systematic measurement of combination drug landscapes to predict in vivo treatment outcomes for tuberculosis

J. Larkins-Ford, T. Greenstein, N. Van, Y. N. Degefu, M. C. Olson, A. Sokolov, B. B. Aldridge

The paper systematically measures drug combinations to predict tuberculosis treatment outcomes using in vitro models and machine learning.

bioRxiv · Feb 3, 2021Microbiology

Morphological profiling of tubercule bacilli identifies drug pathways of action

T. C. Smith, K. M. Pullen, M. C. Olson, M. E. McNellis, I. Richardson, S. Hu, J. Larkins-Ford, X. Wang, J. S. Freundlich, D. M. Ando, B. Aldridge

This paper introduces MorphEUS, a morphological profiling method that classifies drugs based on their effects on Mycobacterium tuberculosis morphology, identifying both primary and off-target pathways of action.

bioRxiv · Mar 11, 2020Microbiology