AFTGAN: prediction of multi-type PPI based on attention free transformer and graph attention network.

ABSTRACT:

Motivation

Protein-protein interaction (PPI) networks and transcriptional regulatory networks are critical in regulating cells and their signaling. A thorough understanding of PPIs can provide more insights into cellular physiology at normal and disease states. Although numerous methods have been proposed to predict PPIs, it is still challenging for interaction prediction between unknown proteins. In this study, a novel neural network named AFTGAN was constructed to predict multi-type PPIs. Regarding feature input, ESM-1b embedding containing much biological information for proteins was added as a protein sequence feature besides amino acid co-occurrence similarity and one-hot coding. An ensemble network was also constructed based on a transformer encoder containing an AFT module (

SUBMITTER: Kang Y 

PROVIDER: S-EPMC9897180 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature