Biomedical data science and AI.
We devise and implement novel data science and AI solutions for biomedical research, particularly when proteomics data is involved. The lab is based in the Digital Environment Research Institute at Queen Mary, University of London and led by Prof Conrad Bessant. We are also affiliated with Queen Mary’s School of Biological and Chemical Sciences and the The Alan Turing Institute.
We are recruiting graduates who are interested in the interface between data science and biomedical research.
As a postdoc: We welcome enquiries from postdocs interested in applying for funding to join our lab. A summary of currently available funding schemes can be found by clicking here. Any enquiries regarding this should be emailed to Conrad Bessant (c.bessant@qmul.ac.uk) with your CV, including “PDRA” in the subject line.
As a PhD student: We host PhD students from the following UK-based doctoral training programmes: LIDo, AIDD and HDiP. Other funded PhD studentship opportunities are available occasionally, typically for candidates from the UK. These will be advertised widely, e.g. on findaphd.com. We are also willing to consider applications from exceptional graduates funded by scholarships, or other means of self-funding, from any country.
As an MSc student: Our MSc Bioinformatics programme is one of the most popular in Europe. Specifically designed for bioscience graduates. Apply now for late September start.
Sadly, we do not have the capacity to host internships or work experience placements at this time.
Branson, N., Cutillas, P.R., Bessant, C., (2024). Comparison of multiple modalities for drug response prediction with learning curves using neural networks and XGBoost. Bioinformatics Advances.
Saihi, H., Bessant, C. and Alazawi, W., (2023). Automated and reproducible cell identification in mass cytometry using neural networks. Briefings in Bioinformatics.
Byrom, B., Bessant, C., Smeraldi, F., Abdollahyan, M., Bridges, Y., Chowdhury, M. and Tahsin, A., (2023). Deriving meaningful aspects of health related to physical activity in chronic disease: concept elicitation using machine-learning-assisted coding of online patient conversations. Value in Health.
Waller, K.J., Saihi, H., Li, W., Brindley, J.H., De Jong. A., Syn, W., Bessant, C., Alazawi, W., (2023). Single-cell phenotypes of peripheral blood immune cells in early and late stages of non-alcoholic fatty liver disease. Clinical and Molecular Hepatology.
Gomez, E.A., Colas, R.A., Souza, P.R., Hands, R., Lewis, M.J., Bessant, C., Pitzalis, C. and Dalli, J., (2020). Blood pro-resolving mediators are linked with synovial pathology and are predictive of DMARD responsiveness in rheumatoid arthritis. Nature Communications, 11(1), pp.1-13.
Gadaleta, E., Fourgoux, P., Pirró, S., Thorn, G.J., Nelan, R., Ironside, A., Rajeeve, V., Cutillas, P.R., Lobley, A.E., Wang, J., Gea, E., Ross-Adams, H., Bessant, C., Lemoine, N.R., Jones, L.J., & Chelala, C. (2020). Characterization of four subtypes in morphologically normal tissue excised proximal and distal to breast cancer. npj Breast Cancer, 6, article number: 38.
Hijazi, M., Smith, R., Rajeeve, V., Bessant, C. & Cutillas, P.R. (2020). Reconstructing kinase network topologies from phosphoproteomics data reveals cancer-associated rewiring. Nature Biotechnology, 38(4), 493–502.
Saha, S., Matthews, D.A., Bessant, C. (2018). High throughput discovery of protein variants using proteomics informed by transcriptomics. Nucleic Acids Research, 46(10), 4893–4902.
Pearce, O.M., Delaine-Smith, R.M., Maniati, E., Nichols, S., Wang, J., Böhm, S., … & Balkwill, F.R. (2018). Deconstruction of a metastatic tumor microenvironment reveals a common matrix response in human cancers. Cancer Discovery, 8(3), 304-319.
Chatzimichali, E.A., & Bessant, C. (2016). Novel application of heuristic optimisation enables the creation and thorough evaluation of robust support vector machine ensembles for machine learning applications. Metabolomics, 12(1), 16.
Evans, V. C., Barker, G., Heesom, K. J., Fan, J., Bessant, C., & Matthews, D. A. (2012). De novo derivation of proteomes from transcriptomes for transcript and protein identification. Nature Methods, 9(12), 1207.
Proteome Informatics (Royal Society of Chemistry, 2016)
Building Bioinformatics Solutions (Oxford University Press, 2014)
Email: c.bessant@qmul.ac.uk
To schedule a call with Conrad, click here.
For other details see Conrad’s official QMUL profile page.