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Cambridge Cardiovascular

Deep Learning in Genomic Medicine


I pursued a degree in Bioinformatics (MSc) at the University of Hamburg after studying Computer Science (BSc) focussing on genome informatics, data mining, and machine learning. Currently, I am a BHF/CRE PhD student in Dr Gräf’s Computational Genomics and Medicine research group at the University of Cambridge with the quest to explore the non-coding space in the genomic data sets, generated by the NIHR BioResource - Rare Diseases (NIHRBR-RD) 13,000 genomes project, via Deep Learning. Recent papers have shown inference from genomic sequence to regulatory markers can be achieved through deep learning. By combining a WGS dataset with cell-type-specific data, we can adjust existing models as well as develop new architectures and increase both their predictive power and accuracy.

AI Scientist - Quotient Therapeutics
BHF PhD student
 Tobias   Tilly

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Person keywords: 
Genomics Rare Disease
Neural Networks
Deep Learning,
pulmonary arterial hypertension
cardiovascular disease