CFAGO: cross-fusion of network and attributes based on attention mechanism for protein function prediction.

ABSTRACT:

Motivation

Protein function annotation is fundamental to understanding biological mechanisms. The abundant genome-scale protein-protein interaction (PPI) networks, together with other protein biological attributes, provide rich information for annotating protein functions. As PPI networks and biological attributes describe protein functions from different perspectives, it is highly challenging to cross-fuse them for protein function prediction. Recently, several methods combine the PPI networks and protein attributes via the graph neural networks (GNNs). However, GNNs may inherit or even magnify the bias caused by noisy edges in PPI networks. Besides, GNNs with stacking of many layers may cause the over-smoothing problem of node representations.

Results

We develop a novel pr

SUBMITTER: Wu Z 

PROVIDER: S-EPMC10032634 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature