NIH R01 · 2025
Developing Novel Deep-Learning Based Methods for Deciphering Non-Coding Gene Regulatory Code
SUMMARY This project will contribute novel deep-learning tools for identification and prioritization of combination of somatic and germline variants that disrupt the gene-regulatory code and are associated with brain and lung cancers. While the effect of genetic mutations within the protein-coding regions is well-studied, the same is not true for those mutations that overlap with the non-coding genomic regions. Non-coding DNA is highly complex due to the existence of polysemy and distant semantic relationship, from a language modeling perspective. To address this challenge, Davuluri and Liu groups successfully developed genome foundation models to model DNA as a language, first on the human…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.