| Predicting retrosynthetic reactions using self-corrected transformer neural networks S Zheng*, J Rao*, Z Zhang, J Xu, Y Yang Journal of Chemical Information and Modeling 60 (1), 47-55, 2019 | 217 | 2019 |
| Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction W Lu, Q Wu, J Zhang, J Rao, C Li, S Zheng Advances in neural information processing systems 35, 7236-7249, 2022 | 113 | 2022 |
| PharmKG: a dedicated knowledge graph benchmark for bomedical data mining S Zheng*, J Rao*, Y Song, J Zhang, X Xiao, EF Fang, Y Yang, Z Niu Briefings in Bioinformatics, 2020 | 109 | 2020 |
| Integrating multi-omics data through deep learning for accurate cancer prognosis prediction H Chai, X Zhou, Z Zhang, J Rao, H Zhao, Y Yang Computers in biology and medicine 134, 104481, 2021 | 100* | 2021 |
| Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networks J Rao, X Zhou, Y Lu, H Zhao, Y Yang Iscience 24 (5), 2021 | 72 | 2021 |
| AlphaFold2-aware protein–DNA binding site prediction using graph transformer Q Yuan, S Chen, J Rao, S Zheng, H Zhao, Y Yang Briefings in Bioinformatics 23 (2), bbab564, 2022 | 63 | 2022 |
| Accurately clustering single-cell RNA-seq data by capturing structural relations between cells through graph convolutional network Y Zeng, X Zhou, J Rao, Y Lu, Y Yang 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2020 | 48 | 2020 |
| Learning attributed graph representations with communicative message passing transformer J Chen, S Zheng, Y Song, J Rao, Y Yang IJCAI-21 (International Joint Conferences on Artificial Intelligence), 2021 | 42 | 2021 |
| Quantitative evaluation of explainable graph neural networks for molecular property prediction J Rao, S Zheng, Y Lu, Y Yang Patterns, 100628, 2022 | 38 | 2022 |
| Accurate prediction of genome-wide RNA secondary structure profile based on extreme gradient boosting Y Ke, J Rao, H Zhao, Y Lu, N Xiao, Y Yang Bioinformatics 36 (17), 4576-4582, 2020 | 10 | 2020 |
| Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction J Rao, S Zheng, S Mai, Y Yang Proceedings of the Thirty-First International Joint Conference on Artificial …, 2022 | 9 | 2022 |
| Retrieval-based Knowledge Augmented Vision Language Pre-training J Rao, Z Shan, L Liu, Y Zhou, Y Yang Proceedings of the 31st ACM International Conference on Multimedia, Pages …, 2023 | 5 | 2023 |
| Cross‐Modal Graph Contrastive Learning with Cellular Images S Zheng*, J Rao*, J Zhang, L Zhou, J Xie, E Cohen, W Lu, C Li, Y Yang Advanced Science, 2404845, 2024 | 4 | 2024 |
| MolRep: A deep representation learning library for molecular property prediction J Rao, S Zheng, Y Song, J Chen, C Li, J Xie, H Yang, H Chen, Y Yang bioRxiv, 2021.01. 13.426489, 2021 | 4 | 2021 |
| Gene based message passing for drug repurposing Y Wang, Z Li, J Rao, Y Yang, Z Dai Iscience 26 (9), 2023 | 3 | 2023 |
| Imputing DNA Methylation by Transferred Learning Based Neural Network XF Wang, X Zhou, JH Rao, ZJ Zhang, YD Yang Journal of Computer Science and Technology 37 (2), 320-329, 2022 | 2 | 2022 |
| A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions J Rao, J Xie, Q Yuan, D Liu, Z Wang, Y Lu, S Zheng, Y Yang Nature Communications 15 (1), 4476, 2024 | | 2024 |
| Self-Supervised Contrastive Molecular Representation Learning with a Chemical Synthesis Knowledge Graph J Xie, Y Wang, J Rao, S Zheng, Y Yang Journal of Chemical Information and Modeling 64 (6), 1945-1954, 2024 | | 2024 |
| Predicting disease-gene associations through self-supervised mutual infomax graph convolution network J Xie, J Rao, J Xie, H Zhao, Y Yang Computers in Biology and Medicine 170, 108048, 2024 | | 2024 |
| Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks J Rao, J Xie, H Lin, S Zheng, Z Wang, Y Yang arXiv preprint arXiv:2402.04710, 2024 | | 2024 |
Yuedong YangSchool of Computer Science and Engineering, Sun Yat-sen University在 mail.sysu.edu.cn 的电子邮件经过验证
