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

Bridge2AI：AI 细胞图谱 (CM4AI) 数据生成项目

• 批准号: 10833742
• 负责人: Jean-Christophe Bélisle-Pipon
• 金额: $ 561.8万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 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/10833742
• 关键词: Address; Architecture; Bridge to Artificial Intelligence; CRISPR/Cas technology; Cells; Cellular Structures; Computer software; Data; Data Set; Disease; Ethics; 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; spatiotemporal; tool; training opportunity

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。

项目成果

Investigating the Influence of Artificial Intelligence on Adolescent Health: An Urgent Call to Action.

调查人工智能对青少年健康的影响：紧急呼吁采取行动。

• 发表时间: 2023-10
• 作者: Brisson, Julien;Bélisle;Ravitsky, Vardit
• 通讯作者: Ravitsky, Vardit

RNA editing: Expanding the potential of RNA therapeutics.

RNA 编辑：扩大 RNA 疗法的潜力。

• 发表时间: 2023-06-07
• 作者: Booth, Brian J;Nourreddine, Sami;Katrekar, Dhruva;Savva, Yiannis;Bose, Debojit;Long, Thomas J;Huss, David J;Mali, Prashant
• 通讯作者: Mali, Prashant

Generative AI, Specific Moral Values: A Closer Look at ChatGPT's New Ethical Implications for Medical AI.

生成式人工智能，特定的道德价值观：仔细研究 ChatGPT 对医疗人工智能的新伦理影响。

• 发表时间: 2023-10
• 作者: Victor, Gavin;Bélisle;Ravitsky, Vardit
• 通讯作者: Ravitsky, Vardit