Machine Learning in cardiovascular research: integrating molecular and phenotypical information in large cohort studies
Fátima Sánchez Cabo
18/03/2021 to 01/01/0001
Host: Javier de las Rivas
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Machine Learning has been around for almost a century. In the last decade, thanks to the collection of large amounts of data, its use has revolution our lives, and is also changing biomedical research and clinical practice. However, understanding the limitations of these methods is of cumbersome importance for its clinical use. Through this talk I will review the main areas of cardiology that are benefitting from machine learning approaches and I will present EN-PESA, a machine learning CV risk score for young, asymptomatic individuals. I will also describe a new deconvolution machine-learning method based on scRNA-Seq data that might improve our understanding of the molecular mechanisms underlying subclinical atherosclerosis.