The prediction accuracy using the proposed representation outperforms a recently introduced representation method that is based only on the distance patterns.
Structure-based methods produce more accurate protein function prediction. In this article, we propose a new protein structure representation for efficient ...
The prediction accuracy using the proposed representation outperforms a recently introduced representation method that is based only on the distance patterns.
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The results show that the proposed representation achieved prediction accuracy up to 98%, with improvement of about 10% on average, and outperforms a ...
May 26, 2021 · Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a ...
Herein, we introduce a new graph model for representing protein structures that exploits the spatial clustering of amino acids and evolutionary profiles of the ...
TAWFN: a deep learning framework for protein function prediction
academic.oup.com › article › btae571
Sep 23, 2024 · This article proposes a novel method that combines multi-layer convolutional neural networks and adaptive graph convolutional neural networks, ...
Nov 18, 2024 · To this end, this work proposes a novel contrast-aware pre-training framework, called SCOP, for protein function prediction. We first design a ...
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In this paper, we propose an effective graph-based protein structure representation learning method, GraSR, for fast and accurate structure comparison. In GraSR ...
Integrating the protein sequence and label representations not only enhances overall function prediction accuracy, but delivers a robust performance of ...
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