E2Efold-3D: end-to-end deep learning method for accurate de novo RNA 3D structure prediction T Shen, Z Hu, Z Peng, J Chen, P Xiong, L Hong, L Zheng, Y Wang, I King, ... arXiv preprint arXiv:2207.01586, 2022 | 32 | 2022 |
Self-supervised contrastive learning for integrative single cell RNA-seq data analysis W Han, Y Cheng, J Chen, H Zhong, Z Hu, S Chen, L Zong, L Hong, ... Briefings in Bioinformatics 23 (5), bbac377, 2022 | 30 | 2022 |
Interpretable RNA foundation model from unannotated data for highly accurate RNA structure and function predictions J Chen, Z Hu, S Sun, Q Tan, Y Wang, Q Yu, L Zong, L Hong, J Xiao, ... arXiv preprint arXiv:2204.00300, 2022 | 27 | 2022 |
fastmsa: Accelerating multiple sequence alignment with dense retrieval on protein language L Hong, S Sun, L Zheng, Q Tan, Y Li bioRxiv, 2021.12. 20.473431, 2021 | 7 | 2021 |
Interpretable rna foundation model from unannotated data for highly accurate rna structure and function predictions. bioRxiv J Chen, Z Hu, S Sun, Q Tan, Y Wang, Q Yu, L Zong, L Hong, J Xiao, ... | 6 | 2022 |
AcrNET: predicting anti-CRISPR with deep learning Y Li, Y Wei, S Xu, Q Tan, L Zong, J Wang, Y Wang, J Chen, L Hong, Y Li Bioinformatics 39 (5), btad259, 2023 | 5 | 2023 |