| Machine learning for drug-target interaction prediction R Chen, X Liu, S Jin, J Lin, J Liu Molecules 23 (9), 2208, 2018 | 286 | 2018 |
| Application of deep learning methods in biological networks S Jin, X Zeng, F Xia, W Huang, X Liu Briefings in bioinformatics 22 (2), 1902-1917, 2021 | 189 | 2021 |
| DeepTTA: a transformer-based model for predicting cancer drug response L Jiang, C Jiang, X Yu, R Fu, S Jin, X Liu Briefings in bioinformatics 23 (3), bbac100, 2022 | 77 | 2022 |
| Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction Y Jiang, S Jin, X Jin, X Xiao, W Wu, X Liu, Q Zhang, X Zeng, G Yang, ... Communications Chemistry 6 (1), 60, 2023 | 50 | 2023 |
| LaGAT: link-aware graph attention network for drug–drug interaction prediction Y Hong, P Luo, S Jin, X Liu Bioinformatics 38 (24), 5406-5412, 2022 | 41 | 2022 |
| A network-based approach to uncover microRNA-mediated disease comorbidities and potential pathobiological implications S Jin, X Zeng, J Fang, J Lin, SY Chan, SC Erzurum, F Cheng NPJ systems biology and applications 5 (1), 41, 2019 | 36 | 2019 |
| Pre‐Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding F Wu, S Jin, Y Jiang, X Jin, B Tang, Z Niu, X Liu, Q Zhang, X Zeng, SZ Li Advanced Science 9 (33), 2203796, 2022 | 30 | 2022 |
| HeTDR: Drug repositioning based on heterogeneous networks and text mining S Jin, Z Niu, C Jiang, W Huang, F Xia, X Jin, X Liu, X Zeng Patterns 2 (8), 2021 | 24 | 2021 |
| preMLI: a pre-trained method to uncover microRNA–lncRNA potential interactions X Yu, L Jiang, S Jin, X Zeng, X Liu Briefings in Bioinformatics 23 (1), bbab470, 2022 | 23 | 2022 |
| Prediction of protein–protein interaction sites based on stratified attentional mechanisms M Tang, L Wu, X Yu, Z Chu, S Jin, J Liu Frontiers in Genetics 12, 784863, 2021 | 18 | 2021 |
| KGNMDA: a knowledge graph neural network method for predicting microbe-disease associations C Jiang, M Tang, S Jin, W Huang, X Liu IEEE/ACM transactions on computational biology and bioinformatics 20 (2 …, 2022 | 14 | 2022 |
| A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks S Jin, Y Hong, L Zeng, Y Jiang, Y Lin, L Wei, Z Yu, X Zeng, X Liu PLOS Computational Biology 19 (11), e1011597, 2023 | 13 | 2023 |
| Chemical structure-aware molecular image representation learning H Xiang, S Jin, X Liu, X Zeng, L Zeng Briefings in Bioinformatics 24 (6), bbad404, 2023 | 12 | 2023 |
| Drug–target interactions prediction via deep collaborative filtering with multiembeddings R Chen, F Xia, B Hu, S Jin, X Liu Briefings in Bioinformatics 23 (2), bbab520, 2022 | 11 | 2022 |
| Improving molecular representation learning with metric learning-enhanced optimal transport F Wu, N Courty, S Jin, SZ Li Patterns 4 (4), 2023 | 10 | 2023 |
| Predict the relationship between gene and large yellow croaker’s economic traits X Zeng, S Jin, J Jiang, K Han, X Min, X Liu Molecules 22 (11), 1978, 2017 | 6 | 2017 |
| An image-enhanced molecular graph representation learning framework H Xiang, S Jin, J Xia, M Zhou, J Wang, L Zeng, X Zeng Proceedings of the Thirty-Third International Joint Conference on Artificial …, 2024 | 3 | 2024 |
| D-Flow: Multi-modality Flow Matching for D-peptide Design F Wu, T Xu, S Jin, X Tang, Z Xu, J Zou, B Hie arXiv preprint arXiv:2411.10618, 2024 | 1 | 2024 |
| Heterogeneous network-based drug repurposing for COVID-19 S Jin, X Zeng, W Huang, F Xia, C Jiang, X Liu, S Peng arXiv preprint arXiv:2107.09217, 2021 | 1 | 2021 |
| RareDR: A Drug Repositioning Approach for Rare Diseases Based on Knowledge Graph Y Huang, S Jin, X Yu, C Jiang, Z Yu, X Liu, S Huang International Conference on Intelligent Computing, 383-394, 2023 | | 2023 |
