Uncovering latent biological function associations through gene set embeddings.

ABSTRACT:

Background

The complexity of biological systems has increasingly been unraveled through computational methods, with biological network analysis now focusing on the construction and exploration of well-defined interaction networks. Traditional graph-theoretical approaches have been instrumental in mapping key biological processes using high-confidence interaction data. However, these methods often struggle with incomplete or/and heterogeneous datasets. In this study, we extend beyond conventional bipartite models by integrating attribute-driven knowledge from the Molecular Signatures Database (MSigDB) using the node2vec algorithm.

Results

Our approach explores unsupervised biological relationships and uncovers potential associations between genes and biological terms through

SUBMITTER: Huang Y 

PROVIDER: S-EPMC11934463 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature