Chris Holmes
Professors of Biostatistics in Genomics
I have a broad interest in the theory, methods and applications of statistics and statistical modelling. My background and beliefs lie in Bayesian statistics which provides a unified framework to stochastic modelling and information processing. I am particularly interested in pattern recognition and nonlinear, nonparametric methods.
Recent publications
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Assessing the Performance of Machine Learning Methods Trained on Public Health Observational Data: A Case Study From COVID-19.
Journal article
Pigoli D. et al, (2024), Stat Med
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Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records.
Journal article
Venkatesh SS. et al, (2024), Nature communications, 15
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Identification and validation of a machine learning model of complete response to radiation in rectal cancer reveals immune infiltrate and TGFβ as key predictors.
Journal article
Domingo E. et al, (2024), EBioMedicine, 106
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A large-scale and PCR-referenced vocal audio dataset for COVID-19
Journal article
Budd J. et al, (2024), Scientific Data, 11
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Equity in medical devices: trainers and educators play a vital role.
Journal article
Whitehead M. et al, (2024), BMJ (Clinical research ed.), 385