ShareNet is a Bayesian information sharing framework for boosting the accuracy of cell type-specific gene regulatory associations from single-cell transcriptomic data. Its design draws upon the intution that many regulatory interactions (and non-interactions) are shared across different cell types. ShareNet adaptively learns a multifactorial information sharing structure that identifies and leverages these shared interactions across a broad range of cell type-to-cell type relationships to enhance network estimates for each cell type in a dataset.

ShareNet will be presented at ISMB 2021 and will appear in an upcoming issue of Bioinformatics.