J
Jianwen Chen
Researcher at Sun Yat-sen University
Publications - 20
Citations - 1062
Jianwen Chen is an academic researcher from Sun Yat-sen University. The author has contributed to research in topics: Medicine & Graph (abstract data type). The author has an hindex of 3, co-authored 13 publications receiving 464 citations.
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Journal ArticleDOI
Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images.
Ying Song,Shuangjia Zheng,Liang Li,Xiang Zhang,Xiaodong Zhang,Ziwang Huang,Jianwen Chen,Ruixuan Wang,Huiying Zhao,Yunfei Zha,Jun Shen,Yutian Chong,Yuedong Yang +12 more
TL;DR: Wang et al. as mentioned in this paper developed a deep learning-based CT diagnosis system to identify patients with COVID-19, which achieved an AUC of 0.99, recall (sensitivity) of 0.,93, and precision of 0,96.
Posted ContentDOI
Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images
Song Ying,Shuangjia Zheng,Liang Li,Xiang Zhang,Xiaodong Zhang,Ziwang Huang,Jianwen Chen,Huiying Zhao,Ruixuan Wang,Yutian Chong,Jun Shen,Yunfei Zha,Yuedong Yang +12 more
TL;DR: A deep learning-based CT diagnosis system (DeepPneumonia) was developed and showed that the established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.
Posted ContentDOI
Structure-aware Protein Solubility Prediction From Sequence Through Graph Convolutional Network And Predicted Contact Map
TL;DR: A new structure-aware method to predict protein solubility by attentive graph convolutional network (GCN), where the protein topology attribute graph was constructed through predicted contact maps from the sequence.
Journal ArticleDOI
Structure-aware protein-protein interaction site prediction using deep graph convolutional network.
TL;DR: Wang et al. as mentioned in this paper proposed a deep graph-based framework GraphPPIS (deep Graph convolutional network for Protein-Protein Interacting Site prediction) for PPI site prediction, where the problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques.
Journal ArticleDOI
Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map
TL;DR: GraphSol as discussed by the authors predicts protein solubility by attentive graph convolutional network (GCN), where the protein topology attribute graph was constructed through predicted contact maps only from the sequence.