Chan Zuckerberg Initiative: Collaborative Computational Tools for the Human Cell Atlas - preproposal

Aim: to create a shared, open reference atlas of all cells in the healthy human body as a resource for studies of health and disease by generating molecular and imaging data. The initiative seeks computational tools, algorithms, visualizations, and benchmark datasets in support of this endeavor, such as:

- standard formats and analysis pipelines for genomic, proteomic, and imaging data,

- web-based interactive visualization of cellular and imaging data,

- tools to extract and analyze data organized by genes, cells, or tissues of interest,

- analytical methods and machine learning approaches to solving problems such as multimodal integration, inference of state transitions and developmental trajectories, spatial representation

- benchmark datasets

- computational approaches to comparing and normalizing genomic and imaging data

- experimental datasets that address computationally-guided questions in quality control, reproducibility, or multimodal integration

Researchers funded by these grants shall work together and share progress. Proposals from areas of machine learning outside of computational biology, e.g. deep learning, are encouraged.

. Funding: not specified. Duration: 1 year

https://chanzuckerberg.com/initiatives/rfa/