Machine learning and systems biology approaches to study the gut microbiome
Developing a machine learning-based analytical pipeline using systems biology approaches to identify microbiome-related features implicated in ageing.
Biography
Matthew is a BBSRC iCASE PhD student 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.
