The National Science Foundation (NSF) awarded a $597,893 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Connecticut (UConn). This 3-year grant, starting on May 1, 2024, is dedicated to developing novel algorithms and computational methods that integrate genomics, pathology, and other multimodal data to build precise disease prediction models. The project aims to advance the state-of-the-art in integrating diverse molecular data types and histology images using advanced artificial intelligence (AI) and deep learning techniques. Key technical objectives include: (i) leveraging graph neural networks to represent and integrate multimodal genomics data with prior biological knowledge, (ii) developing transformer-based methods to identify discriminative regions in pathology images, and (iii) implementing contrastive learning to align features across multimodal omics data for phenotype prediction. This grant supports UConn's expertise in bioinformatics, computational genomics, and medical image analysis to drive innovation in the high-demand, multidisciplinary field of predictive disease modeling.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $597.9k | 4/24/24 |