IHerd: an integrative hierarchical graph representation learning framework to quantify network changes and prioritize risk genes in disease.

ABSTRACT: Different genes form complex networks within cells to carry out critical cellular functions, while network alterations in this process can potentially introduce downstream transcriptome perturbations and phenotypic variations. Therefore, developing efficient and interpretable methods to quantify network changes and pinpoint driver genes across conditions is crucial. We propose a hierarchical graph representation learning method, called iHerd. Given a set of networks, iHerd first hierarchically generates a series of coarsened sub-graphs in a data-driven manner, representing network modules at different resolutions (e.g., the level of signaling pathways). Then, it sequentially learns low-dimensional node representations at all hierarchical levels via efficient graph embedding. Lastly, iHerd

SUBMITTER: Duan Z 

PROVIDER: S-EPMC10513318 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature