SOURCE METADATA
Project: CM4AI
Source ID: nih_reporter_project
Source type: NIH project page
Source URL: https://reporter.nih.gov/project-details/11211616
Raw file: data/raw/CM4AI/reporter_nih_gov_project-details-11211616_row7.txt
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NIH RePORTER Project
Source: https://reporter.nih.gov/project-details/11211616
Application ID: 11211616
Project number: 3OT2OD032742-01S2
Core project number: OT2OD032742
Title: Bridge2AI: Cell Maps for AI (CM4AI) Data Generation Project
Principal investigator: IDEKER, TREY
Organization: UNIVERSITY OF CALIFORNIA, SAN DIEGO
Fiscal year: 2025
Award amount: 5289382
Project start: 2022-09-01T00:00:00
Project end: 2026-08-31T00:00:00

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.

Machine learning (ML) models show great promise in analyzing the human genome
to make predictions, but the inner workings of these models are typically difficult-to-interpret "black boxes." To address this challenge, this Bridge2AI data generation
project will generate a resource of matched data and tools to enable the creation of
"visible" ML systems, which are not black boxes but are built directly on knowledge
maps of cell and tissue architecture.

Preferred terms:
Address;Architecture;Black Box;Bridge to Artificial Intelligence;CRISPR/Cas technology;Cells;Cellular Structures;Computer software;Data;Data Set;Disease;Funding;Generations;Genetic;Genomic medicine;Genotype;Human;Human Genome;Knowledge;Learning;Libraries;Machine Learning;Maps;Mass Spectrum Analysis;Modeling;Phenotype;Proteomics;Research;Resources;System;Text;Tissues;Translations;United States National Institutes of Health;cellular imaging;data management;data standards;machine learning model;precision medicine;programs;research and development;responsible artificial intelligence;spatiotemporal imaging;tool;training opportunity
