Author

Antonia Chalka

Roslin InstituteORCID 0000-0001-7265-5960Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:E. coli· 2 Approach:machine learning (classical)· 2 Assay:whole-genome sequencing· 2 Research Focus:drug response / drug discovery· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Alison S. LowiD· 2 shared David L. GallyiD· 2 shared Adriana Vallejo‐TrujilloiD· 1 shared Aidan T. BrowniD· 1 shared Alba Park de la TorrienteiD· 1 shared Christopher Bellas· 1 shared Gavin K. PatersoniD· 1 shared Ignacio Salinas Valdivieso· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersMicrobiology

Alison S. LowORCID

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2 papers

Recent·Most cited

MicrobiologybioRxivPreprintSep 24, 2026

Integrating bacterial and viral genomic information enhances and extends the machine learning predictions of bacteriophage activity against pathogenic Escherichia coli

Antonia Chalka, Ignacio Salinas Valdivieso, Talal Hossain…·David L. Gally

This paper develops a machine learning model that integrates bacterial and viral genomic data to predict phage activity against E. coli, improving prediction accuracy by incorporating phage gene content.

DOI 10.64898/2026.09.23.753777

E. coliwhole-genome sequencing

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MicrobiologyProceedings of the National Academy of SciencesPeer-reviewedMar 13, 2024

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

Marianne Keith, Alba Park de la Torriente, Antonia Chalka…·David L. Gally

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

54 citationstop 2%

E. coliwhole-genome sequencing

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