TAWFN: a deep learning framework for protein function prediction.

ABSTRACT:

Motivation

Proteins play pivotal roles in biological systems, and precise prediction of their functions is indispensable for practical applications. Despite the surge in protein sequence data facilitated by high-throughput techniques, unraveling the exact functionalities of proteins still demands considerable time and resources. Currently, numerous methods rely on protein sequences for prediction, while methods targeting protein structures are scarce, often employing convolutional neural networks (CNN) or graph convolutional networks (GCNs) individually.

Results

To address these challenges, our approach starts from protein structures and proposes a method that combines CNN and GCN into a unified framework called the two-model adaptive weight fusion network (TAWFN) for protei

SUBMITTER: Meng L 

PROVIDER: S-EPMC11639667 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature