Functional Genomics
Cell Maps for AI (CM4AI) Grand Challenge
The Functional Genomics Grand Challenge seeks to map the spatiotemporal architecture of human cells and use these maps toward the grand challenge of interpretable genotype-phenotype learning. In genomics and precision medicine, machine learning models are often “black boxes,” predicting phenotypes from genotypes without understanding the mechanisms by which such translation occurs. To address this deficiency, this project will launch a coordinated effort involving three complementary mapping approaches – proteomic mass spectrometry, cellular imaging, and genetic perturbation via CRISPR/Cas9 – creating a library of large-scale maps of cellular structure/function across demographic and disease contexts.
Who We Are
Trey Ideker
University of California San Diego
J-C Bélisle-Pipon
Simon Fraser University
Jake Chen
University of Alabama at Birmingham
Timothy Clark
University of Virginia
Nevan Krogan
University of California San Francisco
Emma Lundberg
Stanford University
Prashant Mali
University of California San Diego
Vardit Ravitsky
University of Montreal
Wade Shulz
Principal Investigator
Yale University
Cynthia Brandt
Co-PI
Yale University
Ying Ding
University of Texas at Austin
Sarah Ratcliffe
University of Virginia
Andrej Sali
University of California San Francisco