Dr Christopher Yau

Dr Christopher Yau

Institute of Cancer and Genomic Sciences
Reader in Computational Biology

Contact details

Centre for Computational Biology
Rm 324, Haworth Building (Y2)
University of Birmingham
B15 2TT

Christopher Yau is a Reader in Computational Biology at the Institute of Cancer and Genomic Sciences where he is based at the Centre for Computational Biology and leads the Statistical Machine Learning BioHealth group. 

He is an expert in statistical methodologies for machine learning and data science and specialises in genomic science particularly cancer. His research ranges from mathematical and statistical algorithm development to collaborations with experimental scientists and clinicians involving modelling real world biomedical data sets. He leads a diverse group of researchers who specialise in both experimental and computational modelling and regularly gives talks and lectures around the world on data science. 

His goal is to conceive of a computational intelligence framework that will provide the foundation of learning health systems that will support novel health-related research from the molecular scale through to whole populations. 

His work has been funded by the Medical Research Council, Engineering and Physical Sciences Research Council, Wellcome Trust, Ovarian Cancer Action and Cancer Research UK. 

Christopher is playing a leading role in the development of Statistical Machine Learning for the Genomics England 100,000 Genomes project as sub-domain lead in Machine Learning for the Quantitative Methods, Machine Learning and Functional Genomics Clinical Interpretation Partnership. He sits on the committee of the Statistical Computing Section for the Royal Statistical Society and currently serves as a Career Development Task Force member for the Academy of Medical Sciences.


  • D.Phil (Oxon), Statistics, University of Oxford, 2009
  • M.A. (Hons)/M.Eng. (Cantab), Information and Computer Engineering, University of Cambridge, 2004
  • Fellow of The Royal Statistical Society




Machine Learning



Computational Biology


Other activities

  • Fellow, Alan Turing Institute 
  • Leadership Programme Task Force, Academy of Medical Sciences 
  • Statistical Computing Section Committee, Royal Statistical Society 
  • Statistical Machine Learning sub-domain lead, Genomics England 100,000 Genomes Project


Recent publications


Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) Consortium, Mourikis, TP, Benedetti, L, Foxall, E, Temelkovski, D, Nulsen, J, Perner, J, Cereda, M, Lagergren, J, Howell, M, Yau, C, Fitzgerald, RC, Scaffidi, P & Ciccarelli, FD 2019, 'Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma', Nature Communications, vol. 10, 3101. https://doi.org/10.1038/s41467-019-10898-3

Campbell, K & Yau, C 2018, 'A descriptive marker gene approach to single-cell pseudotime inference', Bioinformatics. https://doi.org/10.1093/bioinformatics/bty498

Zheng, Y, Sethi, R, Mangala, LS, Taylor, C, Goldsmith, J, Wang, M, Masuda, K, Karaminejadranjbar, M, Mannion, D, Miranda, F, Herrero-Gonzalez, S, Hellner, K, Chen, F, Alsaadi, A, Albukhari, A, Fotso, DC, Yau, C, Jiang, D, Pradeep, S, Rodriguez-Aguayo, C, Lopez-Berestein, G, Knapp, S, Gray, NS, Campo, L, Myers, KA, Dhar, S, Ferguson, D, Bast, RC, Sood, AK, von Delft, F & Ahmed, AA 2018, 'Tuning microtubule dynamics to enhance cancer therapy by modulating FER-mediated CRMP2 phosphorylation', Nature Communications, vol. 9, no. 1, 476. https://doi.org/10.1038/s41467-017-02811-7

Campbell, KR & Yau, C 2018, 'Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data', Nature Communications, vol. 9, no. 1, 2442. https://doi.org/10.1038/s41467-018-04696-6

Hu, Z, Yau, C & Ahmed, AA 2017, 'A pan-cancer genome-wide analysis reveals tumour dependencies by induction of nonsense-mediated decay', Nature Communications, vol. 8, 15943. https://doi.org/10.1038/ncomms15943

Wang, J, Mouradov, D, Wang, X, Jorissen, RN, Chambers, MC, Zimmerman, LJ, Vasaikar, S, Love, C, Li, S, Lowes, K, Leuchowius, K-J, Jousset, H, Weinstock, J, Yau, C, Mariadason, J, Shi, Z, Ban, Y, Chen, X, Coffey, RJC, Slebos, RJC, Burgess, AW, Liebler, DC, Zhang, B & Sieber, OM 2017, 'Colorectal cancer cell line proteomes are representative of primary tumors and predict drug sensitivity', Gastroenterology. https://doi.org/10.1053/j.gastro.2017.06.008

Campbell, K & Yau, C 2017, 'Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers', Wellcome Open Research, vol. 2, no. 19. https://doi.org/10.12688/wellcomeopenres.11087.1

Rigoni, A, Poulsom, R, Jeffery, R, Mehta, S, Lewis, A, Yau, C, Giannoulatou, E, Feakins, R, Lindsay, JO, Colombo, MP & Silver, A 2017, 'Separation of Dual Oxidase 2 and Lactoperoxidase Expression in Intestinal Crypts and Species Differences May Limit Hydrogen Peroxide Scavenging During Mucosal Healing in Mice and Humans', Inflammatory Bowel Diseases, vol. 24, no. 1, pp. 136-148. https://doi.org/10.1093/ibd/izx024

Titsias, MK & Yau, C 2017, 'The hamming ball sampler', Journal of American Statistical Association, vol. 112, no. 520, pp. 1598-1611. https://doi.org/10.1080/01621459.2016.1222288

Campbell, KR & Yau, C 2017, 'switchde: inference of switch-like differential expression along single-cell trajectories', Bioinformatics, vol. 33, no. 8, pp. 1241-1242. https://doi.org/10.1093/bioinformatics/btw798

Campbell, KR & Yau, C 2016, 'Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference', PLoS Computational Biology, vol. 12, no. 11, e1005212. https://doi.org/10.1371/journal.pcbi.1005212


CONSORT-AI steering group & SPIRIT-AI Steering Group 2019, 'Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed', Nature Medicine, vol. 25, no. 10, pp. 1467-1468. https://doi.org/10.1038/s41591-019-0603-3

Conference article

Law, HCL, Yau, C & Sejdinovic, D 2017, 'Testing and learning on distributions with symmetric noise invariance', Advances in Neural Information Processing Systems, vol. 2017-December, pp. 1344-1354. <http://papers.nips.cc/paper/6733-testing-and-learning-on-distributions-with-symmetric-noise-invariance.pdf>

Conference contribution

Rukat, T, Holmes, CC & Yau, C 2018, Probabilistic boolean tensor decomposition. in J Dy & A Krause (eds), Volume 80: International Conference on Machine Learning, 10-15 July 2018, Stockholmsmässan, Stockholm Sweden. Proceedings of Machine Learning Research, vol. 80, Proceedings of Machine Learning Research, pp. 4413-4422, The 35th International Conference on Machine Learning (ICML 2018) , Stockholm , Sweden, 10/07/18. <http://proceedings.mlr.press/v80/rukat18a.html>

Review article

Yau, C & Campbell, K 2019, 'Bayesian statistical learning for big data biology', Biophysical Reviews, vol. 11, pp. 95-102. https://doi.org/10.1007/s12551-019-00499-1

View all publications in research portal