Project Grant R03DE034770

The National Institute of Dental and Craniofacial Research awarded the University of New Hampshire $299,950 on August 19, 2026, under the Oral Diseases and Disorders Research program (CFDA 93.121) to develop a functional smoothed deep learning approach combining deep neural networks and functional data analysis techniques for analyzing high-dimensional genetic data associated with orofacial clefts and predicting disease risk. The research will build and validate a functional smoothed deep learning (FSDL) method using functional data analysis techniques to improve disease risk prediction for orofacial clefts based on high-dimensional genetic data, with capability to capture dependent structures among genetic variants and handle both gene sequencing data and conventional covariates such as demographic data. The project will employ larger-scale orofacial clefts databases for training and validation. The award supports work at the University of New Hampshire in New Hampshire with a performance period from August 19, 2026, through August 18, 2028. The research is led by an early-stage new investigator with a multidisciplinary team experienced in statistics, computer science, biostatistics, and orofacial clefts. This is a Project Grant, the standard NIH research project grant assistance type.