Double Fun In March! March 29th at 12:00 EDT Cory Mclean & Farhad Hormozdiari will discuss "DeepNull models non-linear covariate effects to improve phenotypic prediction and association power." (Nat Commun 2022) #IGESJournalClub Register Here: harvard.zoom.us/meeting/regist…

Excited to share new phenotyping methods for COPD, improving COPD GWAS Collaborative work with IU and BWH, co-authors Justin Cosentino, B Besaz , @babak_alipanahi, Z McCaw, last author Farhad Hormozdiari, eng manager C McLean. Paper: medrxiv.org/content/10.110… Code: github.com/Google-Health/…

Interested in ML based phenotyping. Check our paper.

Our work on Structural Variations discovery from accurate long reads (PacBio HiFi) is out (nature.com/articles/s4159…). Joint work with Luca Denti Parsoa Khorsand Paola Bonizzoni Rayan Chikhi Hormozdiari Lab

SVDSS combines multiple strategies to improve detection of structural variation using low-error long-read sequencing data. Luca Denti Parsoa Khorsand Hormozdiari Lab Paola Bonizzoni PANGAIA Project Rayan Chikhi nature.com/articles/s4159…

Our recent work on SV calling using HiFi PacBio reads is out!

Introducing DeepNull by McCaw et al. (special shout out to Farhad Hormozdiari and Andrew Carroll) #deeplearning #gwas #genomics

Super excited to present our deep learning works in health at AGBTAg as a keynote. #GoogleHealth #GoogIeResearch #deeplearning

Super excited that our paper is out! Amazing collaboration with Brigham and Women's Hospital, Harvard Medical School, Northeastern U. , and IU School of Medicine. Google AI Google Research

Deep learning model improves COPD risk prediction and gene discovery | Nature Genetics bit.ly/undefined #ai #ml #dl

Interested in performing GWAS on high-dimensional clinical data and how to utilize deep learning to improve GWAS power. Check our new paper (medrxiv.org/content/10.110…).

"Rare penetrant mutations confer severe risk of common diseases" medrxiv.org/content/10.110…

Thank you so much for the invite - really looking forward to what should be a great conference.

📢New in AJHG from McCaw et al. 📰An allelic-series rare-variant association test for candidate-gene discovery 👇 rb.gy/zx99a

Medicine is inherently multimodal, so our #AI systems must be too. Our Health AI team at Google Research is making progress with alternative approaches to introducing multimodal capabilities to medical #LLMs: Google AI ai.googleblog.com/2023/08/multim…

Super excited about our work in multimodal data & genetic architecture.

Med-Gemini-Polygenic is the first LMM to predict health outcomes from genomic data converted to polygenic risk scores. It beats PRS linear models, even surprisingly predicts outcomes it wasn't trained for.

Introducing Med-Gemini, our new family of AI research models for medicine, building on Gemini's advanced capabilities. We've achieved state-of-the-art performance on a variety of benchmarks and unlocked novel applications. #MedGemini #AIResearch #MedicalAI

Super excited to see our team's work on unsupervised phenotyping published (nature.com/articles/s4158……). Ted Yun and I wrote a blog explaining the work and results.

M-REGLE is an AI method that simultaneously analyzes multiple health data streams, including ECGs and PPGs, to jointly learn rich representations and significantly boost the discovery of genetic links to disease. Learn all about it at goo.gle/4ehDwp4