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

作为NIH共同基金Bridge 2AI计划的一部分，CM 4AI数据生成 该项目旨在绘制人类细胞的时空结构，并利用这些 映射到可解释的基因型-表型学习的巨大挑战。在 基因组学和精准医学，机器学习模型往往是“黑匣子”， 从基因型预测表型，而不了解机制， 这样的翻译发生。为了解决这一缺陷，项目将启动一个 涉及三种互补作图方法的协调努力-蛋白质组 质谱，细胞成像，和通过CRISPR/Cas9基因干扰， 创建一个大规模的细胞结构/功能图库， 人口和疾病背景。这些数据将广泛刺激研究， 通过多尺度单元和 组织结构除了数据和工具，该项目还将实施一个 基于公平访问和软件原则的标准数据管理方法， 具有用于表示细胞图的深度起源和复制包， 他们的基础数据集;启动道德人工智能的研究计划，特别是因为它 涉及图谱如何用于基因组医学和模型解释;以及 在新兴领域刺激多元化的培训机会组合 生物机器学习

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.