Dr. Nicole Wheeler

Postdoctoral Fellow - Machine Learning

nicole.wheeler@cgps.group

Uses statistics and machine learning to gain novel insights from data.

Learn more about my work

Nicole's work involves mining and modelling pathogen phenotypic, genomic and epidemiological data for the identification and surveillance of high-risk strains of bacterial pathogens. She aims to develop intelligent alerting systems for flagging the emergence of high-risk strains of bacteria, and guide the subsequent use of whole-genome sequencing (WGS) to investigate outbreaks.

Nicole has also recently joined OUTBREAK, a world-first surveillance system designed to combat the growing threat of antimicrobial resistance in Australia and across the world. Powered by artificial intelligence, OUTBREAK will use new sensor technologies and huge data sets to track, trace and tackle antibiotic-resistant infections, helping us to save lives.

Publications

Wheeler, N.E., Sánchez-Busó, L., Argimón, S. & Jeffrey, B. Lean, mean, learning machines. Nature Reviews Microbiology, 2020. Read Me

Bawn, M., Thilliez, G., Kirkwood, M., Wheeler, N., Petrovska, L., Dallman, T. J., ... & Kingsley, R. A.. Evolution of Salmonella enterica serotype Typhimurium driven by anthropogenic selection and niche adaptation. PLoS Genetics, 2020. Read Me

Lees, J. A., Tien Mai, T., Galardini, M., Wheeler, N. E., & Corander, J. Improved inference and prediction of bacterial genotype-phenotype associations using pangenome-spanning regressions. BioRxiv, 2019. Read Me

Van Puyvelde, S., Pickard, D., Vandelannoote, K., Heinz, E., Barbé, B., de Block, T., Clare, S., Coomber, E. L., Harcourt, K., ... Wheeler, N. E., ... An African Salmonella Typhimurium ST313 sublineage with extensive drug-resistance and signatures of host adaptation. Nature Communications, 2019. Read Me

Wheeler, N. E., Reuter, S., Chewapreecha, C., Lees, J. A., Blane, B., Horner, C., … Peacock, S. J. Contrasting approaches to genome-wide association studies impact the detection of resistance mechanisms in Staphylococcus aureus. BioRxiv, 2019. Read Me

Hicks. A.L., Wheeler, N.E., Sanchez-Buso, L., Rakeman, J.L., Harris, S.R., Grad, Y.H. Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data. PLoS Computational Biology, 2019. Read Me

Lees, J.A., Ferwerda, B., Kremer, P.H.C., Wheeler, N.E., Valls Serón, M., Croucher, N.J., Gladstone, R.A., Bootsma, H., Rots, N., Z... Joint sequencing of human and pathogen genomes reveals the genetics of pneumococcal meningitis. Nature Communications, 2019. Read Me

Wheeler, N.E., Gardner, P.P., Barquist, L. Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica. PLoS Genetics, 2018. Read Me

Wheeler, N.E., Barquist, L., Kingsley, R.A., Gardner, P.P. A profile-based method for identifying functional divergence of orthologous proteins in bacterial genomes. Bioinformatics, 2016. Read Me

Our Research Alliances and Funders

CDC

Centers for Disease
Control and Prevention

ECDC

European Centre for Disease Prevention and Control

FAO

Food and Agriculture Organisation
of the United Nations

NIH

National Institutes
for Health

NIHR

National Institute for
Health Research

WHO

World Health Organisation

Big Data Institute logoOxford University logoWellcome Trust logo
Wellcome Sanger Institute logoWellcome Genome Campus logo

Ⓒ 2019 Centre for Genomic Pathogen Surveillance