| m6A-Atlas: a comprehensive knowledgebase for unraveling the N6-methyladenosine (m6A) epitranscriptome Y Tang, K Chen, B Song, J Ma, X Wu, Q Xu, Z Wei, J Su, G Liu, R Rong, ... Nucleic acids research 49 (D1), D134-D143, 2021 | 250 | 2021 |
| Stepwise feature fusion: Local guides global J Wang, Q Huang, F Tang, J Meng, J Su, S Song International conference on medical image computing and computer-assisted …, 2022 | 248 | 2022 |
| WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach K Chen, Z Wei, Q Zhang, X Wu, R Rong, Z Lu, J Su, JP De Magalhães, ... Nucleic acids research 47 (7), e41-e41, 2019 | 208 | 2019 |
| m7GHub: deciphering the location, regulation and pathogenesis of internal mRNA N7-methylguanosine (m7G) sites in human B Song, Y Tang, K Chen, Z Wei, R Rong, Z Lu, J Su, JP De Magalhães, ... Bioinformatics 36 (11), 3528-3536, 2020 | 106 | 2020 |
| DuAT: Dual-aggregation transformer network for medical image segmentation F Tang, Z Xu, Q Huang, J Wang, X Hou, J Su, J Liu Chinese Conference on Pattern Recognition and Computer Vision (PRCV), 343-356, 2023 | 98 | 2023 |
| Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications Z Song, D Huang, B Song, K Chen, Y Song, G Liu, J Su, JP Magalhães, ... Nature communications 12 (1), 4011, 2021 | 93 | 2021 |
| m5C-Atlas: a comprehensive database for decoding and annotating the 5-methylcytosine (m5C) epitranscriptome J Ma, B Song, Z Wei, D Huang, Y Zhang, J Su, JP De Magalhães, ... Nucleic Acids Research 50 (D1), D196-D203, 2022 | 82 | 2022 |
| RMDisease: a database of genetic variants that affect RNA modifications, with implications for epitranscriptome pathogenesis K Chen, B Song, Y Tang, Z Wei, Q Xu, J Su, JP De Magalhães, DJ Rigden, ... Nucleic acids research 49 (D1), D1396-D1404, 2021 | 82 | 2021 |
| Application of deep q-network in portfolio management Z Gao, Y Gao, Y Hu, Z Jiang, J Su 2020 5th IEEE International Conference on Big Data Analytics (ICBDA), 268-275, 2020 | 78 | 2020 |
| Bioinformatics approaches for deciphering the epitranscriptome: recent progress and emerging topics L Liu, B Song, J Ma, Y Song, SY Zhang, Y Tang, X Wu, Z Wei, K Chen, ... Computational and structural biotechnology journal 18, 1587-1604, 2020 | 58 | 2020 |
| ConsRM: collection and large-scale prediction of the evolutionarily conserved RNA methylation sites, with implications for the functional epitranscriptome B Song, K Chen, Y Tang, Z Wei, J Su, JP De Magalhães, DJ Rigden, ... Briefings in Bioinformatics 22 (6), bbab088, 2021 | 45 | 2021 |
| Chromosome classification with convolutional neural network based deep learning W Zhang, S Song, T Bai, Y Zhao, F Ma, J Su, L Yu 2018 11th international congress on image and signal processing, biomedical …, 2018 | 44 | 2018 |
| PIANO: a web server for pseudouridine-site (Ψ) identification and functional annotation B Song, Y Tang, Z Wei, G Liu, J Su, J Meng, K Chen Frontiers in genetics 11, 88, 2020 | 37 | 2020 |
| m6Acomet: large-scale functional prediction of individual m6A RNA methylation sites from an RNA co-methylation network X Wu, Z Wei, K Chen, Q Zhang, J Su, H Liu, L Zhang, J Meng BMC bioinformatics 20, 1-12, 2019 | 37 | 2019 |
| m6A-TSHub: Unveiling the Context-Specific m6A Methylation and m6A-Affecting Mutations in 23 Human Tissues B Song, D Huang, Y Zhang, Z Wei, J Su, J Pedro de Magalhães, ... Genomics, proteomics & bioinformatics 21 (4), 678-694, 2023 | 33 | 2023 |
| News2vec: News network embedding with subnode information Y Ma, L Zong, Y Yang, J Su Proceedings of the 2019 conference on empirical methods in natural language …, 2019 | 33 | 2019 |
| A novel XGBoost method to identify cancer tissue-of-origin based on copy number variations Y Zhang, T Feng, S Wang, R Dong, J Yang, J Su, B Wang Frontiers in genetics 11, 585029, 2020 | 31 | 2020 |
| Adaptive active contour model based automatic tongue image segmentation J Guo, Y Yang, Q Wu, J Su, F Ma 2016 9th International Congress on image and signal processing, BioMedical …, 2016 | 31 | 2016 |
| Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data D Huang, B Song, J Wei, J Su, F Coenen, J Meng Bioinformatics 37 (Supplement_1), i222-i230, 2021 | 30 | 2021 |
| Intention understanding in human–robot interaction based on visual-nlp semantics Z Li, Y Mu, Z Sun, S Song, J Su, J Zhang Frontiers in Neurorobotics 14, 610139, 2021 | 27 | 2021 |