PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications.

ABSTRACT: Recently, large-scale scRNA-seq datasets have been generated to understand the complex and poorly understood signaling mechanisms within microenvironment of Alzheimer's Disease (AD), which are critical for identifying novel therapeutic targets and precision medicine. Though a set of targets have been identified, however, it remains a challenging to infer the core intra- and inter-multi-cell signaling communication networks using the scRNA-seq data, considering the complex and highly interactive background signaling network. Herein, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and signaling communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on

SUBMITTER: Feng J 

PROVIDER: S-EPMC10827077 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature