Learning a generalized graph transformer for protein function prediction in dissimilar sequences.

ABSTRACT:

Background

In the face of a growing disparity between high-throughput sequence data and low-throughput experimental studies, the emerging field of deep learning stands as a promising alternative. Generally, many data-driven approaches are capable of facilitating fast and accurate predictions of protein functions. Nevertheless, the inherent statistical nature of deep learning techniques may limit their generalization capabilities when applied to novel nonhomologous proteins that diverge significantly from existing ones.

Results

In this work, we herein propose a novel, generalized approach named Graph Adversarial Learning with Alignment (GALA) for protein function prediction. Our GALA method integrates a graph transformer architecture with an attention pooling module to extrac

SUBMITTER: Fu Y 

PROVIDER: S-EPMC11734293 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature