| 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 | 30 | 2019 |
| Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networks J Rao, X Zhou, Y Lu, H Zhao, Y Yang biorxiv, 2020 | 3 | 2020 |
| Integrating multi-omics data with deep learning for predicting cancer prognosis H Chai, X Zhou, Z Cui, J Rao, Z Hu, Y Lu, H Zhao, Y Yang bioRxiv, 807214, 2019 | 3 | 2019 |
| 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 | 1 | 2020 |
| 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 | | 2021 |
| 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 | | 2020 |
| 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 IEEE International Conference on Bioinformatics and Biomedicine, 2020 | | 2020 |
