Author

A. L. Hicks

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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Approach:statistical model· 3 Assay:whole-genome sequencing· 3 Organism:human· 2 Research Focus:variant effect prediction· 2 Organism:bacteria (other)· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Y. H. Grad· 4 shared L. Sanchez-Buso· 3 shared K. C. Ma· 2 shared N. E. Wheeler· 2 shared S. R. Harris· 2 shared T. D. Mortimer· 2 shared D. A. Williamson· 1 shared D. Golparian· 1 shared

Activity by Year

2019-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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4 shared papersMicrobiology

Y. H. Grad

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Author profile →

4 papers

Recent·Most cited

Increased antibiotic susceptibility in Neisseria gonorrhoeae through adaptation to the cervical environment

K. C. Ma, T. D. Mortimer, A. L. Hicks, N. E. Wheeler, L. Sanchez-Buso, D. Golparian, G. Taiaroa, D. H. Rubin, Y. Wang, D. A. Williamson, M. Unemo, S. R. Harris, Y. H. Grad

This study identifies loss-of-function mutations in efflux pumps as a mechanism for increased antibiotic susceptibility in Neisseria gonorrhoeae cervical isolates.

Nature Communications · Aug 17, 2020Peer-reviewedMicrobiology

Increased power from bacterial genome-wide association conditional on known effects identifies Neisseria gonorrhoeae macrolide resistance mutations in the 50S ribosomal protein L4

K. C. Ma, T. D. Mortimer, M. A. Duckett, A. L. Hicks, N. E. Wheeler, L. Sanchez-Buso, Y. H. Grad

This study uses conditional GWAS to identify a new azithromycin resistance mutation in Neisseria gonorrhoeae, demonstrating its prevalence and geographic spread.

bioRxiv · Mar 24, 2020Microbiology

Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data

A. L. Hicks, N. Wheeler, L. Sanchez-Buso, J. L. Rakeman, S. R. Harris, Y. H. Grad

This study evaluates factors affecting machine learning models' performance in predicting antibiotic resistance from whole genome sequencing data across multiple bacterial species.

PLOS Computational Biology · Sep 3, 2019Peer-reviewedMicrobiology

Quantifying the surveillance required to sustain genetic marker-based antibiotic resistance diagnostics

A. L. Hicks, S. M. Kissler, M. Lipsitch, Y. H. Grad

The paper quantifies the surveillance needed to maintain the effectiveness of genetic marker-based antibiotic resistance diagnostics by identifying sampling and testing rates for early detection of novel resistance variants.

bioRxiv · Jul 11, 2019 · PreprintMicrobiology