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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Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis
Schoenpflug LA. et al, (2025), The Journal of Pathology, 265, 184 - 197
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Development and assessment of a machine learning tool for predicting emergency admission in Scotland.
Liley J. et al, (2024), NPJ digital medicine, 7
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Publisher Correction: Development and assessment of a machine learning tool for predicting emergency admission in Scotland.
Liley J. et al, (2024), NPJ digital medicine, 7
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Assessing the Performance of Machine Learning Methods Trained on Public Health Observational Data: A Case Study From COVID-19.
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.
Venkatesh SS. et al, (2024), Nature communications, 15