NIH R01 · 2025
Machine learning methods for interpreting spatial multi-omics data
PROJECT SUMMARY The proposed research program aims to develop innovative computational tools for the analysis and integration of data from emerging spatially-resolved genomic technologies, which have the potential to uncover the role of interactions with the environment in normal development and disease. Existing analytical tools for analyzing spatial omics data are limited in their interpretability and are not capable of integrating multi-modal data. Leveraging our extensive experience in computational modeling of single-cell data as another high-dimensional genomic data type, we will design machine learning frameworks in the form of probabilistic and deep generative models to tackle the…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.