This $306,999 Project Grant award from the National Institutes of Health (NIH) Office of the Director under the Trans-NIH Research Support program (CFDA 93.310) is focused on developing an integrated machine learning framework to accurately identify lung cancer subtypes. The key products and services to be delivered include: Establishing a gene signature-transfer machine learning model that leverages large-scale bulk and single-cell transcriptomics data, both within and beyond NIH Common Fund data sets, to identify well-annotated lung cancer subtypes as well as explore novel subtypes by detecting rare cell types. Developing a multi-omics integration framework to systematically combine single-cell and bulk multi-omics data, including genomics, transcriptomics, and epigenetics, to further boost the accuracy of lung cancer subtype identification, even when only partial or incomplete multi-omics data are available for new patients. The award was granted to the University of Nebraska Medical Center, a prominent academic research institution with extensive capabilities in biomedical research and federal contract/grant execution. The project aims to have a direct impact on improving downstream lung cancer risk stratification, diagnosis, prognosis, and treatment optimization, with the potential to be customized and extended to identifying subtypes of other cancer types.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $307.0k | 9/5/24 |