ExPecto predicts tissue-specific expression and variant effect ab initio from sequence, prioritizes all GWAS variants, generates in silico mutagenesis of all TSS proximal regions, and provides a link to evolutionary constraints on gene expression. rdcu.be/233k

Great description of our work out at Nature Genetics: "AI Accurately Predicts Effects of Genetic Mutations in Biological Dark Matter" simonsfoundation.org/2018/07/16/ai-… Flatiron Institute @FlatironCCB Simons Foundation Princeton Engineering Princeton Computer Science Princeton University Research

Our latest paper on noncoding contributions in #autism using deep learning models is out in Nature Genetics. De novo noncoding variants contribute to # of cases comparable to coding. With Bob Darnell (@darnelr.bsky. & bob.darnell@Threads) Jian Zhou Chris Park chandralt4 Simons Foundation Princeton University #WGS rdcu.be/bEAto

Hi genomics community! I recently started my own group at UTSW. We are looking for postdoc and students interested in machine learning and genomics to join us in our adventures in new frontiers! Please RT or forward to any potential interested candidates zhoulab.io/home#join-us

SARS-CoV-2 receptor networks in diabetic kidney disease, BK-Virus nephropathy and COVID-19 associated acute kidney injury medrxiv.org/cgi/content/sh… #medRxiv

thrilled to be a part of this collaboration and apply our cell-type specific networks at HumanBase to COVID-19.

Check out humanbase.io - from tissue-specific networks to mutational regulatory impact to disease gene prediction. Project lead by the amazing Aaron Wong Nicely described in simonsfoundation.org/2020/11/10/fin… Simons Foundation Flatiron Institute @FlatironCCB

It's #OlgaFest today in Michigan! 😀 Olga Troyanskaya's academic children & grandchildren are speaking in today's Women+ Data Science webinar hosted jointly by University of Michigan and MSU!

Combing through millions of possible protein combinations to assemble a catalog that could be used to precisely target only cancer cells while leaving normal ones alone. Kudos to rachid elalouani UC San Francisco Olga Troyanskaya Troyanskaya Lab ow.ly/QXMh50CylWC

High-throughput experiments generate large data sets that allow the study of multicellular complexity; #MachineLearning can help analyse, interpret and model these datasets. Read more in the Review by Rachel Sealfon, Aaron Wong & Olga Troyanskaya: go.nature.com/3hJ0ucT

Check out our review of machine learning methods to model multicellular complexity!

Our new preprint (w/ Jian Zhou) is out! 🎉 We developed sequence classes, which allow for easily interpretable yet systematic quantification of the regulatory activities for any sequence & variant, using deep learning-based sequence modeling. (1/13) doi.org/10.1101/2021.0…

Check out our latest review in NRG on decoding disease - from genomes to networks to phenotypes! nature.com/articles/s4157…

Decoding disease: from genomes to networks to phenotypes go.nature.com/3fq7VnT #Review by Aaron Wong, Rachel S. G. Sealfon, Chandra L. Theesfeld & Olga Troyanskaya Troyanskaya Lab @FlatironCCB Flatiron Institute Princeton University Princeton beehive

Machine learning in materials science! All our articles on machine learning in one collection, featuring E. Kumacheva Group Alan Aspuru-Guzik Troyanskaya Lab Muratahan Aykol Berend Smit Adam Gormley Michael Webb Natalia Ares group Martin Krallinger and many more - Check it out 😎go.nature.com/3gdwuVw

Kathy Chen’s paper for sequence-based global map of regulatory activity is now out (nature.com/articles/s4158…)! Congratulations to all co-authors Aaron Wong Olga Troyanskaya. Thanks to Anshul Kundaje (anshulkundaje@bluesky) and Wouter Meuleman for the News & Views!

The Sei web server on humanbase (hb.flatironinstitute.org/sei) is now updated to add the capability of computing Sei predictions including sequence class scores for any user-provided sequences or variants (big thanks to Aaron Wong!)

CS Prof. Olga Troyanskaya and team presented big-data approaches to drug development at the "Celebrate Princeton Invention" event. Olga Troyanskaya princeton.edu/news/2017/11/1…

Great talk from Olga Troyanskaya on predicting effects of mutations with DeepSEA and now being able to predict tissue specific gene expression with new ExPecto approach. Valuable for GWAS causal variant prioritization and de novo calling of disease variants #T2G18

Proud of our discovery of noncoding regulatory signal in autism. Independently confirmed in discovery and replication cohorts for both DNA and RNA regulation. #deeplearning #WGS #autism #ASD biorxiv.org/content/early/…