Project Grant 2405416

This $741,002 federal Project Grant award from the National Science Foundation's Biological Sciences program supports the development of a novel machine learning framework for analyzing large-scale, multi-modal single-cell biological data. The project aims to construct advanced computational tools and user-friendly software to enable more effective extraction of insights and knowledge from complex single-cell datasets spanning genomics, transcriptomics, epigenomics, and proteomics. The research team at the University of Maryland, College Park will investigate techniques such as deep canonical correlation analysis, semi-supervised neural networks, and variational autoencoders to integrate and interpret these high-dimensional, multi-omic datasets. The resulting bioinformatics software and analytical methods are expected to significantly benefit the broader biological research community by facilitating hypothesis testing and knowledge discovery from large-scale single-cell data. No subawards are planned under this award, which has an expected completion date of August 31, 2025.