| DUNet: A deformable network for retinal vessel segmentation Q Jin, Z Meng, TD Pham, Q Chen, L Wei, R Su Knowledge-Based Systems 178, 149-162, 2019 | 710 | 2019 |
| RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans Q Jin, Z Meng, C Sun, H Cui, R Su Frontiers in Bioengineering and Biotechnology 8, 605132, 2020 | 365 | 2020 |
| ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides L Wei, C Zhou, H Chen, J Song, R Su Bioinformatics 34 (23), 4007-4016, 2018 | 351 | 2018 |
| Improved prediction of protein–protein interactions using novel negative samples, features, and an ensemble classifier L Wei, P Xing, J Zeng, JX Chen, R Su, F Guo Artificial Intelligence in Medicine 83, 67-74, 2017 | 235 | 2017 |
| Prediction of human protein subcellular localization using deep learning L Wei, Y Ding, R Su, J Tang, Q Zou Journal of Parallel and Distributed Computing 117, 212-217, 2018 | 222 | 2018 |
| Deep-Resp-Forest: a deep forest model to predict anti-cancer drug response R Su, X Liu, L Wei, Q Zou Methods 166, 91-102, 2019 | 205 | 2019 |
| M6APred-EL: a sequence-based predictor for identifying N6-methyladenosine sites using ensemble learning L Wei, H Chen, R Su Molecular Therapy-Nucleic Acids 12, 635-644, 2018 | 165 | 2018 |
| CPPred-RF: a sequence-based predictor for identifying cell-penetrating peptides and their uptake efficiency L Wei, PW Xing, R Su, G Shi, ZS Ma, Q Zou Journal of Proteome Research 16 (5), 2044-2053, 2017 | 165 | 2017 |
| Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species L Wei, S Luan, LAE Nagai, R Su, Q Zou Bioinformatics 35 (8), 1326-1333, 2019 | 163 | 2019 |
| Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites L Wei, R Su, B Wang, X Li, Q Zou, X Gao Neurocomputing 324, 3-9, 2019 | 141 | 2019 |
| Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools R Su, J Hu, Q Zou, B Manavalan, L Wei Briefings in bioinformatics 21 (2), 408-420, 2020 | 133 | 2020 |
| Developing a multi-dose computational model for drug-induced hepatotoxicity prediction based on toxicogenomics data R Su, H Wu, B Xu, X Liu, L Wei IEEE/ACM Transactions on computational biology and bioinformatics 16 (4 …, 2018 | 130 | 2018 |
| PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning L Wei, C Zhou, R Su, Q Zou Bioinformatics 35 (21), 4272-4280, 2019 | 125 | 2019 |
| Iterative feature representations improve N4-methylcytosine site prediction L Wei, R Su, S Luan, Z Liao, B Manavalan, Q Zou, X Shi Bioinformatics 35 (23), 4930-4937, 2019 | 124 | 2019 |
| ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides B Rao, C Zhou, G Zhang, R Su, L Wei Briefings in bioinformatics 21 (5), 1846-1855, 2020 | 115 | 2020 |
| Decision variants for the automatic determination of optimal feature subset in RF-RFE Q Chen, Z Meng, X Liu, Q Jin, R Su Genes 9 (6), 301, 2018 | 115 | 2018 |
| Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods K Kandasamy, JKC Chuah, R Su, P Huang, KG Eng, S Xiong, Y Li, ... Scientific reports 5 (1), 12337, 2015 | 114 | 2015 |
| CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning X Qiang, C Zhou, X Ye, P Du, R Su, L Wei Briefings in Bioinformatics 21 (1), 11-23, 2020 | 110 | 2020 |
| Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms L Wei, J Hu, F Li, J Song, R Su, Q Zou Briefings in Bioinformatics 21 (1), 106-119, 2020 | 107 | 2020 |
| M6AMRFS: robust prediction of N6-methyladenosine sites with sequence-based features in multiple species X Qiang, H Chen, X Ye, R Su, L Wei Frontiers in genetics 9, 495, 2018 | 94 | 2018 |
