Biography.
Matthew is a BBSRC iCASE PhD student within the Korcsmáros group, where together with BenevolentAI, Matthew is developing machine learning (ML) and systems biology approaches to investigate the role of the microbiome during the ageing process.
By harnessing the power of ML, these approaches will be used to identify prognostic indicators from metagenomics and metatranscriptomics data. Combining ML-based features with host-microbiome interactions and systems biology, we aim to improve our understanding of how the microbiota contributes to our health.
Matthew started his research at the Earlham Institute during the second year of undergraduate studies where he was introduced to systems biology. As an intern within the Korcsmáros Group, he developed an algorithm to identify and contextualise autophagy-related proteins within a molecular interaction network.
This experience led to his final year research project, in which he created an integrated network-medicine and ML pipeline to identify prognosis indicators in ulcerative colitis. These short-term projects gave him the motivation and passion to further explore the capabilities of ML within life sciences.
Publications.
Feb 2020
Seyed Tabib N., Madgwick M., Sudhakar P., Verstockt B., Korcsmaros T., Vermeire S.
Big data in IBD: big progress for clinical practice.
Gut
Publishers version: doi:http://dx.doi.org/10.1136/gutjnl-2019-320065
Mar 2020
Kirkup B. M., McKee A. M., Madgwick M., Price C. P., Dreger S. A., Makin K. A., Caim S., Le Gall G., Paveley J., Leclaire C., Dalby M., Alcon-Giner C., Andrusaite A., Di Modica M., Triulzi T., Tagliabue E., Milling S. W. F., Weilbaecher K. N., Korcsmáros T., Hall L. J., Robinson S. D.
Antibiotic-induced disturbances of the gut microbiota result in accelerated breast tumour growth via a mast cell-dependent pathway
https://www.biorxiv.org/content/10.1101/2020.03.07.982108v1.full
Publishers version: doi:10.1101/2020.03.07.982108
