SLiMs prediction method based on enhanced attention mechanism and feature fusion.

ABSTRACT:

Motivation

Short linear motifs (SLiMs) are functional regions composed of short sequences of specific amino acids. They usually do not have independent 3D three-dimensional structures, but play important roles in biological processes. Traditional detection methods have high cost and heavy workload, therefore it is necessary to seek an accurate detection method for SLiMs.

Results

In this paper, we propose a new SLiMs prediction method, named EMAF_SLiMs, based on enhanced attention mechanism and feature fusion. We calculate three features sets which contain semantic embedding, physicochemical characteristic and evolutionary information. Then, we design the enhanced attention model based on SwiftFormer to highlight the characteristic of SLiMs. In addition, the multi-head attent

SUBMITTER: Hao Y 

PROVIDER: S-EPMC12782102 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature