Joint embedding of biological networks for cross-species functional alignment.

ABSTRACT:

Motivation

Model organisms are widely used to better understand the molecular causes of human disease. While sequence similarity greatly aids this cross-species transfer, sequence similarity does not imply functional similarity, and thus, several current approaches incorporate protein-protein interactions to help map findings between species. Existing transfer methods either formulate the alignment problem as a matching problem which pits network features against known orthology, or more recently, as a joint embedding problem.

Results

We propose a novel state-of-the-art joint embedding solution: Embeddings to Network Alignment (ETNA). ETNA generates individual network embeddings based on network topological structure and then uses a Natural Language Processing-inspired cross

SUBMITTER: Li L 

PROVIDER: S-EPMC10477935 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature