3DTI-Net: Learn 3D Transform-Invariant Feature Using Hierarchical Graph CNN

2019 ◽  
pp. 37-51
Author(s):  
Guanghua Pan ◽  
Peilin Liu ◽  
Jun Wang ◽  
Rendong Ying ◽  
Fei Wen
Keyword(s):  
Hierarchical Graph ◽  
Invariant Feature

scholarly journals GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues

2021 ◽  
Author(s):  
Ying Xia ◽  
Chun-Qiu Xia ◽  
Xiaoyong Pan ◽  
Hong-Bin Shen
Keyword(s):  
Neural Networks ◽  
Nucleic Acid ◽  
Biological Activities ◽  
New Drugs ◽  
Superior Performance ◽  
Nucleic Acid Binding ◽  
Hierarchical Graph ◽  
Binding Residue ◽  
Binding Residues ◽  
Graph Neural Networks

Abstract Knowledge of the interactions between proteins and nucleic acids is the basis of understanding various biological activities and designing new drugs. How to accurately identify the nucleic-acid-binding residues remains a challenging task. In this paper, we propose an accurate predictor, GraphBind, for identifying nucleic-acid-binding residues on proteins based on an end-to-end graph neural network. Considering that binding sites often behave in highly conservative patterns on local tertiary structures, we first construct graphs based on the structural contexts of target residues and their spatial neighborhood. Then, hierarchical graph neural networks (HGNNs) are used to embed the latent local patterns of structural and bio-physicochemical characteristics for binding residue recognition. We comprehensively evaluate GraphBind on DNA/RNA benchmark datasets. The results demonstrate the superior performance of GraphBind than state-of-the-art methods. Moreover, GraphBind is extended to other ligand-binding residue prediction to verify its generalization capability. Web server of GraphBind is freely available at http://www.csbio.sjtu.edu.cn/bioinf/GraphBind/.

scholarly journals ComQA: Compositional Question Answering via Hierarchical Graph Neural Networks

2021 ◽  
Author(s):  
Bingning Wang ◽  
Ting Yao ◽  
Weipeng Chen ◽  
Jingfang Xu ◽  
Xiaochuan Wang
Keyword(s):  
Neural Networks ◽  
Question Answering ◽  
Hierarchical Graph ◽  
Graph Neural Networks
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