Author

A. S. Low

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 Immunology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Research Focus:differential expression· 1 Organism:E. coli· 1 Organism:human· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1

Frequent Co-Authors

G. K. Paterson· 2 shared A. Chalka· 1 shared A. E. Carpenter· 1 shared A. Park de la Torriente· 1 shared A. Vallejo-Trujillo· 1 shared B. A. Earnshaw· 1 shared B. Fogelson· 1 shared B. L. Probst· 1 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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2 shared papersMicrobiology

G. K. Paterson

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Predictive phage therapy for Escherichia coli urinary tract infections: cocktail selection for therapy based on machine learning models

M. Keith, A. Park de la Torriente, A. Chalka, A. Vallejo-Trujillo, S. P. McAteer, G. K. Paterson, A. S. Low, D. L. Gally

This paper uses machine learning to predict effective phage cocktails for treating E. coli urinary tract infections based on bacterial genome data.

bioRxiv · Nov 23, 2023 · PreprintMicrobiology

Rapid metagenomic sequencing for diagnosis and antimicrobial sensitivity prediction of canine bacterial infections

N. Ring, A. S. Low, B. Wee, G. K. Paterson, T. Nuttall, R. Mellanby, D. Gally, J. R. Fitzgerald

This paper presents a rapid metagenomic sequencing pipeline for diagnosing canine bacterial infections and predicting antimicrobial sensitivity within 5 hours.

bioRxiv · Jan 30, 2023Microbiology

Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery

M. F. Cuccarese, B. A. Earnshaw, K. Heiser, B. Fogelson, C. T. Davis, P. F. McLean, H. B. Gordon, K.-R. Skelly, F. L. Weathersby, V. Rodic, I. K. Quigley, E. D. Pastuzyn, B. M. Mendivil, N. H. Lazar, C. A. Brooks, , C. C. Gibson

This paper develops a deep-learning enabled phenomics platform to identify immune-modulating compounds and applies it to study SARS-CoV-2 infection and cytokine storm, identifying clinically relevant compounds and new drug candidates.

bioRxiv · Aug 2, 2020 · PreprintImmunology