| Protein–protein interaction site prediction through combining local and global features with deep neural networks M Zeng, F Zhang, FX Wu, Y Li, J Wang, M Li Bioinformatics 36 (4), 1114-1120, 2020 | 181 | 2020 |
| Effective prediction of three common diseases by combining SMOTE with Tomek links technique for imbalanced medical data M Zeng, B Zou, F Wei, X Liu, L Wang 2016 IEEE International Conference of Online Analysis and Computing Science …, 2016 | 134 | 2016 |
| Automated ICD-9 coding via a deep learning approach M Li, Z Fei, M Zeng, FX Wu, Y Li, Y Pan, J Wang IEEE/ACM transactions on computational biology and bioinformatics 16 (4 …, 2018 | 127 | 2018 |
| Automatic ICD-9 coding via deep transfer learning M Zeng, M Li, Z Fei, Y Yu, Y Pan, J Wang Neurocomputing 324, 43-50, 2019 | 103 | 2019 |
| Network-based methods for predicting essential genes or proteins: a survey X Li, W Li, M Zeng, R Zheng, M Li Briefings in bioinformatics 21 (2), 566-583, 2020 | 102 | 2020 |
| A deep learning framework for identifying essential proteins by integrating multiple types of biological information M Zeng, M Li, Z Fei, FX Wu, Y Li, Y Pan, J Wang IEEE/ACM transactions on computational biology and bioinformatics 18 (1 …, 2019 | 80 | 2019 |
| DeepFunc: a deep learning framework for accurate prediction of protein functions from protein sequences and interactions F Zhang, H Song, M Zeng, Y Li, L Kurgan, M Li Proteomics 19 (12), 1900019, 2019 | 76 | 2019 |
| SDLDA: lncRNA-disease association prediction based on singular value decomposition and deep learning M Zeng, C Lu, F Zhang, Y Li, FX Wu, Y Li, M Li Methods 179, 73-80, 2020 | 63 | 2020 |
| DeepEP: a deep learning framework for identifying essential proteins M Zeng, M Li, FX Wu, Y Li, Y Pan BMC bioinformatics 20, 1-10, 2019 | 49 | 2019 |
| Deep matrix factorization improves prediction of human circRNA-disease associations C Lu, M Zeng, F Zhang, FX Wu, M Li, J Wang IEEE Journal of Biomedical and Health Informatics 25 (3), 891-899, 2020 | 46 | 2020 |
| DMFLDA: a deep learning framework for predicting lncRNA–disease associations M Zeng, C Lu, Z Fei, FX Wu, Y Li, J Wang, M Li IEEE/ACM transactions on computational biology and bioinformatics 18 (6 …, 2020 | 37 | 2020 |
| DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding M Zeng, Y Wu, C Lu, F Zhang, FX Wu, M Li Briefings in Bioinformatics 23 (1), bbab360, 2022 | 33 | 2022 |
| BridgeDPI: a novel graph neural network for predicting drug–protein interactions Y Wu, M Gao, M Zeng, J Zhang, M Li Bioinformatics 38 (9), 2571-2578, 2022 | 28 | 2022 |
| Improving circRNA–disease association prediction by sequence and ontology representations with convolutional and recurrent neural networks C Lu, M Zeng, FX Wu, M Li, J Wang Bioinformatics 36 (24), 5656-5664, 2020 | 27 | 2020 |
| NEDD: a network embedding based method for predicting drug-disease associations R Zhou, Z Lu, H Luo, J Xiang, M Zeng, M Li BMC bioinformatics 21 (13), 1-12, 2020 | 27 | 2020 |
| A deep learning framework for gene ontology annotations with sequence-and network-based information F Zhang, H Song, M Zeng, FX Wu, Y Li, Y Pan, M Li IEEE/ACM transactions on computational biology and bioinformatics 18 (6 …, 2020 | 23 | 2020 |
| Kaicd: A knowledge attention-based deep learning framework for automatic icd coding Y Wu, M Zeng, Z Fei, Y Yu, FX Wu, M Li Neurocomputing 469, 376-383, 2022 | 18 | 2022 |
| DeepPPF: A deep learning framework for predicting protein family SM Yusuf, F Zhang, M Zeng, M Li Neurocomputing 428, 19-29, 2021 | 17 | 2021 |
| Essential protein prediction based on node2vec and xgboost N Wang, M Zeng, Y Li, FX Wu, M Li Journal of Computational Biology 28 (7), 687-700, 2021 | 15 | 2021 |
| PROBselect: accurate prediction of protein-binding residues from proteins sequences via dynamic predictor selection F Zhang, W Shi, J Zhang, M Zeng, M Li, L Kurgan Bioinformatics 36 (Supplement_2), i735-i744, 2020 | 14 | 2020 |
Min LiSchool of Computer Science and Engineering, Central South University在 mail.csu.edu.cn 的电子邮件经过验证
