Cell Maps for AI
Bridge2AI project seeks to map the spatiotemporal architecture of human cells and use these maps towards 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. .
PI: Jake Chen
CO-PI: Ying Ding
The Data Acquisition module will generate comprehensive datasets for protein targets of interest using three primary platforms: 1) Protein interaction mapping via affinity purification coupled to mass spectrometry to determine the interactome; 2) Spatial proteomics via imaging to map spatial subcellular coordinates; and 3) Genetic perturbation mapping to determine the functional states associated with the target via single-cell CRISPR-Cas perturbation screens.
PI: Emma Lundberg
Co-PI: Prashant Mali
The Tools module addresses three aims:
- Dissemination of project data and cell maps.
- Creation of tools for building cell maps from the three primary data streams.
- Creation of tools that enable cell maps to power a range of AI/ML applications in the biomedical community.
All three of these aims will be supported by acquisition of Graphical Processing Unit (GPU) computing hardware, which will ramp up each year in anticipation of use in training and pilot use by ML researchers
PI: Jean-Christophe Bélisle-Pipon, Vardit Ravitsky
Co-PI: Yael Bensoussan
The Standards module will provide a reusable, cloud-based informatics platform and tools for FAIR data and software interoperability standards implementation across the CM4AI Data Generation Project, based on the FAIRSCAPE digital commons framework.