A multimodal deep learning model to infer cell-type-specific functional gene networks.

ABSTRACT:

Background

Functional gene networks (FGNs) capture functional relationships among genes that vary across tissues and cell types. Construction of cell-type-specific FGNs enables the understanding of cell-type-specific functional gene relationships and insights into genetic mechanisms of human diseases in disease-relevant cell types. However, most existing FGNs were developed without consideration of specific cell types within tissues.

Results

In this study, we created a multimodal deep learning model (MDLCN) to predict cell-type-specific FGNs in the human brain by integrating single-nuclei gene expression data with global protein interaction networks. We systematically evaluated the prediction performance of the MDLCN and showed its superior performance compared to two baseli

SUBMITTER: Afshar S 

PROVIDER: S-EPMC9926713 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature