Understanding the genetic factors that underlie the normal variation in cardiac anatomy is of great interest. In this study, Rodrigo Bonazzola et al. applied unsupervised geometric deep learning to phenotype the left ventricle using an MRI-derived three-dimensional mesh representation (as depicted on the cover). We show that this approach boosts genetic discovery and provides deeper insights into the genetic underpinnings of cardiac morphology. Check out https://lnkd.in/edrvTg2W

Tanveer Syeda-Mahmood’s research featured in cover story for Nature Medicine Intelligence

Understanding the genetic factors that underlie the normal variation in cardiac anatomy is of great interest. In this study, Rodrigo Bonazzola et al. applied unsupervised geometric deep learning to phenotype the left ventricle using an MRI-derived three-dimensional mesh representation (as depicted on the cover). We show that this approach boosts genetic discovery and provides deeper insights into the genetic underpinnings of cardiac morphology.

Check out https://lnkd.in/edrvTg2W

A new $5 million grant from the Warren Alpert Foundation was recently awarded to the Department of Biomedical Data Science (DBDS) at the Stanford School of Medicine. The grant will fund the training of 15 graduate scholars over the next five years to enhance training and retention of scholars in computational biology/artificial intelligence (CBAI).

$5 Million Warren Alpert Foundation Grant To Fund 15 Department of Biomedical Data Science Computational Biology/AI Scholars

A new $5 million grant from the Warren Alpert Foundation was recently awarded to the Department of Biomedical Data Science (DBDS) at the Stanford School of Medicine. The grant will fund the training of 15 graduate scholars over the next five years to enhance training and retention of scholars in computational biology/artificial intelligence (CBAI).

Read the story here: https://dbds.stanford.edu/five-million-warren-alpert-foundation-to-fund-15-computational-biology-ai-scholars/