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

Bridge2AI：AI 细胞图谱 (CM4AI) 数据生成项目

基本信息

批准号：
10473403
负责人：
Jean-Christophe Bélisle-Pipon
金额：
$ 489.45万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10473403
关键词：
Architecture Cells Genomics Maps Text

项目摘要

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.

作为 NIH 共同基金 Bridge2AI 计划的一部分，CM4AI 数据生成该项目旨在绘制人类细胞的时空结构图并利用这些映射到可解释的基因型-表型学习的巨大挑战。基因组学和精准医学，机器学习模型通常是“黑匣子”，在不了解机制的情况下根据基因型预测表型为了解决这个缺陷，项目将启动一个翻译项目。涉及三种互补作图方法的协调工作——蛋白质组学通过 CRISPR/Cas9 进行质谱分析、细胞成像和遗传扰动 – 创建一个大规模的细胞结构/功能图谱库这些数据将广泛刺激研究和研究。由多尺度细胞和信息提供信息的“可见”机器学习系统的开发除了数据和工具之外，该项目还将实施一个组织架构。基于公平访问和软件原则的标准数据管理方法，具有经过验证的深度和复制包，用于表示细胞图谱和他们的基础数据集；启动道德人工智能研究计划，尤其是涉及如何在基因组医学和模型解释中使用图谱；以及刺激新兴领域的多样化培训机会生物机器学习。