Genetic analyses of eight complex diseases using predicted continuous representations of disease dlvr.it/TM6v0l

How can ML models trained on electronic health records revolutionize the way we assess genetic variant penetrance beyond traditional binary disease classifications?Science Magazine Icahn School of Medicine at Mount Sinai "Machine learning–based penetrance of genetic variants" • Traditional genetic

Insightful perspective on our ML penetrance paper on penetrance and variant consequences, in Science Magazine nicely written by Harper Raiken and Amelie Stein science.org/doi/10.1126/sc…

AI + routine labs help clarify genetic risk. Mount Sinai Genetics modeled 1,600 variants across 10 diseases using 1M+ health records, moving past binary labels. mountsinai.org/about/newsroom… #precisionmedicine #genetics #AIinHealthcare #AIinMedicine Iain S. Forrest, MD-PhD Ron Do

Grateful for the shoutout Eric Topol - Thank you for spotlighting our work!

It took a village! Forever grateful to Ron Do Lab Icahn School of Medicine at Mount Sinai including Daniel M Jordan, Ph.D. Ben Omega Petrazzini and collaborators Girish Nadkarni Judy Cho Wendy Chung. No better way to kick off internal medicine residency Hopkins Bayview IM Residency JohnsHopkinsBayview Johns Hopkins University 💪

👇🏾 Excellent tweetorial by lead author @iainForrest of our recent Science Magazine paper on machine learning–based penetrance.

🚨“Pathogenic” genetic variant does NOT = guaranteed disease 🚨 Yet, doctors and patients are still left to decide what to do with the label… In our new Nature Genetics perspective, we explain why this is and how we can improve genetic risk interpretation🧵