Imperial College London

DrSamGreenbury

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Research Associate
 
 
 
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Contact

 

s.greenbury

 
 
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Location

 

126Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Summary

Sam Greenbury is a postdoctoral researcher within the ITMAT Data Science Group, developing machine learning methods for application to electronic health records. His work utilises population-level data from the National Neonatal Research Database (NNRD) to generate novel and translatable insights into clinical care.

He is affiliated with the EPSRC Centre for Mathematics of Precision Healthcare (CMPH), where his postdoctoral research developed novel and interpretable methods for inference and prediction of pathways and trajectories in disease and other complex systems.

He received his PhD in the field of computational evolutionary biology from the University of Cambridge in 2014 under the supervision of Sebastian Ahnert situated within the TCM Group. He previously read Physics at the University of Oxford where he undertook his Master's research in 2009 within the Ard Louis Research Group. His Master's and PhD theses focussed on general principles and properties that underpin the mapping between genotype and phenotype (the GP map) in systems governed by biological evolution.

Subsequent to his PhD, he worked at the Department of Health until 2016, developing and leading analysis on national policy priorities for both social care and maternity services.

Selected Publications

Journal Articles

Greenbury SF, Barahona M, Johnston IG, 2020, HyperTraPS: Inferring Probabilistic Patterns of Trait Acquisition in Evolutionary and Disease Progression Pathways., Cell Syst, Vol:10, Pages:39-51.e10

Johnston I, Hoffmann T, Greenbury S, et al., 2019, Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data, Npj Digital Medicine, Vol:2, ISSN:2398-6352

Greenbury SF, Schaper S, Ahnert SE, et al., 2016, Genetic correlations greatly increase mutational robustness and can both reduce and enhance evolvability, Plos Computational Biology, Vol:12, ISSN:1553-7358

Greenbury SF, Ahnert SE, 2015, The organization of biological sequences into constrained and unconstrained parts determines fundamental properties of genotype–phenotype maps, Journal of the Royal Society Interface, Vol:12, ISSN:1742-5689

Greenbury SF, Johnston IG, Louis AA, et al., 2014, A tractable genotype-phenotype map modelling the self-assembly of protein quaternary structure, Journal of the Royal Society Interface, Vol:11, ISSN:1742-5689

Greenbury SF, Johnston IG, Smith MA, et al., 2010, The effect of scale-free topology on the robustness and evolvability of genetic regulatory networks, Journal of Theoretical Biology, Vol:267, ISSN:0022-5193, Pages:48-61

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