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NOBEL PRIZE 2026

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NIHOFFICE OF THE DIRECTOR, NATIONAL INSTITUTES OF HEALTH

Bridge2AI: Cell Maps for AI (CM4AI) Data Generation Project

Completed
Institution
UNIVERSITY OF CALIFORNIA, SAN DIEGO, CA
Principal investigator
Trey Ideker
Funding
$5.3M
Duration
2022–2026
Open portfolio

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Still being scored

Abstract

As part of the NIH Common Fund’s Bridge2AI program, the CM4AI data generation project 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, 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. These data will broadly stimulate research and development in "visible" machine learning systems informed by multi-scale cell and tissue architecture. In addition to data and tools, this project will implement a standards data management approach based on FAIR access and software principles, with deep provenance and replication packages for representation of cell maps and their underlying datasets; initiate a research program in ethical AI, especially as it relates to how maps will be used in genomic medicine and model interpretation; and stimulate a diverse portfolio of training opportunities in the emerging field of biomachine learning.

Research team

  • Jean-Christophe Bélisle-PiponPI
  • Jake Yue ChenPI
  • Timothy W ClarkPI
  • Trey IdekerPI
  • Nevan J KroganPI
  • Emma LundbergPI
  • Prashant MaliPI
  • Sarah J RatcliffePI
  • Vardit RavitskyPI
  • Wade Loren SchulzPI

LOADING MOONBASE SCORE

OFFICE OF THE DIRECTOR, NATIONAL INSTITUTES OF HEALTHOT2Data Coordination, Mapping, and Modeling[DCMM]architecturecellular imaginggenomic medicinemapsgenotype-phenotypecommon fundstimulatedata generation

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