Xiaokun Li
7 Papers
Xiaokun Li is an academic researcher. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 1, co-authored 1 publications.
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Papers
Multi-modality attribute learning-based method for drug-protein interaction prediction based on deep neural network
TL;DR: In this paper , a multi-modality attributes learning-based framework for drug-protein interaction prediction with molecular transformer and graph convolutional networks was proposed, which extracted intermolecular sub-structural information and chemical semantic representations from biomedical data.
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Rapid Identification of Wild Gentiana Genus in Different Geographical Locations Based on FT-IR and an Improved Neural Network Structure Double-Net
TL;DR: A neural network is designed, Double-Net, to predict the geographical locations of wild Gentiana Genus plants via FT-IR spectroscopy, and it is shown that the accuracy of the neural network structure Double- net can reach 100%, and the F1_score can reach 1.0.
Multi-scale convolutional networks for traffic forecasting with spatial-temporal attention
TL;DR: Wang et al. as discussed by the authors proposed a novel Multi-Scale Convolutional Networks (MSCN), an end-to-end solution to solve traffic forecasting problem, which employs an encoder with spatial-temporal attention mechanism to model both spatial and temporal correlations.
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Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases
TL;DR: This manuscript proposed a creatively semi-supervised model named bidirectional generative adversarial network for miRNA-disease association prediction (BGANMDA), which was employed in three different case studies to validate its prediction capability and accuracy.
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SAGDTI: self-attention and graph neural network with multiple information representations for the prediction of drug-target interactions
Xiaokun Li,Qiang Yang,Gongning Luo,Longpeng Xu,Weihe Dong,Wei Wang,Suyu Dong,Kuanquan Wang,Ping Xuan,Xin Gao +9 more
TL;DR: SAGDTI is the first method to sufficiently consider the unique molecular attribute representations for both drugs and targets in the input form of the SMILES sequences and three-dimensional structure graphs and outperforms existing prediction models.
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