Knowledge graph embedding for profiling the interaction between transcription factors and their target genes.

ABSTRACT: Interactions between transcription factor and target gene form the main part of gene regulation network in human, which are still complicating factors in biological research. Specifically, for nearly half of those interactions recorded in established database, their interaction types are yet to be confirmed. Although several computational methods exist to predict gene interactions and their type, there is still no method available to predict them solely based on topology information. To this end, we proposed here a graph-based prediction model called KGE-TGI and trained in a multi-task learning manner on a knowledge graph that we specially constructed for this problem. The KGE-TGI model relies on topology information rather than being driven by gene expression data. In this paper, we formu

SUBMITTER: Wu YH 

PROVIDER: S-EPMC10313080 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature