This two-year, $156,911 Project Grant from the National Science Foundation's Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), will fund research applying graph neural networks, self-supervised learning, and meta learning techniques to cancer multi-omics data and driver discovery. Specifically, the awardee, Oakland University, will construct a graph neural network model incorporating biological domain knowledge to identify rare cancer drivers with less labeled training data. Self-supervised learning will pre-train the model on multi-omics cancer data, and meta learning will apply the model to a pan-cancer dataset of dozens of cancer types to improve generalizability and detect novel drivers across cancer types. The results are intended to advance pan-cancer integrative analysis and establish a repeatable process for addressing other difficult biological problems through multi-data integration and machine learning.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $156.9k | 3/29/23 |