| 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 | 723 | 2019 |
| Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA Q Zou, P Xing, L Wei, B Liu Rna 25 (2), 205-218, 2019 | 479 | 2019 |
| 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 | 360 | 2018 |
| Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information L Wei, J Tang, Q Zou Information Sciences 384, 135-144, 2017 | 256 | 2017 |
| Improved and promising identification of human microRNAs by incorporating a high-quality negative set L Wei, M Liao, Y Gao, R Ji, Z He, Q Zou IEEE/ACM transactions on computational biology and bioinformatics 11 (1 …, 2013 | 250 | 2013 |
| 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 | 223 | 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 | 213 | 2019 |
| mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation B Manavalan, S Basith, TH Shin, L Wei, G Lee Bioinformatics 35 (16), 2757-2765, 2019 | 210 | 2019 |
| A novel hierarchical selective ensemble classifier with bioinformatics application L Wei, S Wan, J Guo, KKL Wong Artificial intelligence in medicine 83, 82-90, 2017 | 205 | 2017 |
| Meta-4mCpred: a sequence-based meta-predictor for accurate DNA 4mC site prediction using effective feature representation B Manavalan, S Basith, TH Shin, L Wei, G Lee Molecular Therapy-Nucleic Acids 16, 733-744, 2019 | 202 | 2019 |
| Fast prediction of protein methylation sites using a sequence-based feature selection technique L Wei, P Xing, G Shi, Z Ji, Q Zou IEEE/ACM Transactions on Computational Biology and Bioinformatics 16 (4 …, 2017 | 196 | 2017 |
| 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 | 175 | 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, 2018 | 166 | 2018 |
| 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 | 166 | 2018 |
| 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 | 142 | 2019 |
| Identifying enhancer–promoter interactions with neural network based on pre-trained DNA vectors and attention mechanism Z Hong, X Zeng, L Wei, X Liu Bioinformatics 36 (4), 1037-1043, 2020 | 141 | 2020 |
| 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 | 135 | 2020 |
| 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 | 132 | 2019 |
| 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 |
Quan Zou (邹权)University of Electronic Science and Technology of China在 nclab.net 的电子邮件经过验证
