Hosted by the School of Medicine at Cardiff University
Large scale medical genomic research relies on high quality genomics and clinical data. This talk describes some of the data handling issues with increasingly sparse large-scale genomic sequencing data and present a tool developed to reduce genomic data dimensionality and provide a gene-level pathogenic score – GenePy. This tool generates a pathogenic burden score for all individuals for all genes. Some use cases demonstrating its utility in detecting clinically significant signals are presented across various phenotypes – rare disease, common disease and complex traits. Switching to clinical data, the talk will describe the use of Artifical Intelligence in the form of a large language model, applied to 30,000+ histopathology and imaging records to generate standardised data fit for integration with genomic data.
Register via Eventbrite
Sarah Ennis is Professor of Genomics running the University of Southampton Genomic Informatics Research Laboratory. With ~200 published research papers, her research focuses on the analysis of genomic data for the purposes of (novel) disease gene detection, genomic diagnostics, prognostics and prediction; and the development, testing and application of methods to optimise genomic data modelling. She is Chief Investigator of NIHR studies recruiting patients and extracting high-integrity longitudinal clinical data from electronic patient records. Research projects use these local data in addition to Genomics England and UKBiobank datasets. Sarah is a Research Director within the Central & South Genomics Medicine Service and a project-lead within the Genomic AI Network of Excellence. Across these roles, she works closely with Bioinformatics Research Centers (BRCs) and Secure Data Environments (SDEs) to reduce barriers to increase the value of genomic data to patients and public.