Contact information
Viktoria Brunner
DPhil student
I am a computational biology PhD student working on antimicrobial resistance prediction in the Fowler group. We are part of the wider group of modernising medical microbiology at the Nuffield Department of Medicine, University of Oxford.
The overall aim of our research is to improve susceptibility prediction for antibiotics used in treatment of tuberculosis and other infectious diseases. To achieve this goal, we use the large anonymised clinical datasets available in-house for various pathogens to better the prediction of susceptibility to different antibiotics. We use machine learning and statistical modelling to achieve this goal. Additionally, we use the clinical data to get insights into the genetic determinants underlying antibiotic resistance and its spread.Those two things combined could be an important step towards tackling the spread of resistance and thereby the threat of a post-antibiotic era.
I mostly code in Python, but have conducted previous projects in R, MatLab and Java. Although I was formally trained as a biologist, I enjoy using computational tools to direct my research.
Websites
Recent publications
Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network
Journal article
Dissanayake D. et al, (2026), BMC Microbiology
Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network
Preprint
Dissanayake D. et al, (2025)
Subpopulations in clinical samples of M. tuberculosis can give rise to rifampicin resistance and shed light on how resistance is acquired
Journal article
Brunner VM. and Fowler PW., (2025), JAC-Antimicrobial Resistance, 7
Subpopulations in clinical samples of M. tuberculosis can give rise to rifampicin resistance and shed light on how resistance is acquired
Preprint
Brunner VM. and Fowler PW., (2025)
Compensatory mutations are associated with increased in vitro growth in resistant clinical samples of Mycobacterium tuberculosis
Journal article
Brunner VM. and Fowler PW., (2024), Microbial Genomics, 10
