| 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 | 198 | 2020 |
| 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 | 87 | 2019 |
| Deep convolutional neural network for automatically segmenting acute ischemic stroke lesion in multi-modality MRI L Liu, S Chen, F Zhang, FX Wu, Y Pan, J Wang Neural Computing and Applications 32, 6545-6558, 2020 | 81 | 2020 |
| 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 | 69 | 2020 |
| 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 | 54 | 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 | 46 | 2022 |
| DeepDISOBind: accurate prediction of RNA-, DNA-and protein-binding intrinsically disordered residues with deep multi-task learning F Zhang, B Zhao, W Shi, M Li, L Kurgan Briefings in bioinformatics 23 (1), bbab521, 2022 | 43 | 2022 |
| 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 | 27 | 2020 |
| DeepPPF: A deep learning framework for predicting protein family SM Yusuf, F Zhang, M Zeng, M Li Neurocomputing 428, 19-29, 2021 | 19 | 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 | 18 | 2020 |
| DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning Y Li, M Zeng, F Zhang, FX Wu, M Li Bioinformatics 39 (1), btac779, 2023 | 7 | 2023 |
| LncRNA–disease association prediction through combining linear and non-linear features with matrix factorization and deep learning techniques M Zeng, C Lu, F Zhang, Z Lu, FX Wu, Y Li, M Li 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2019 | 7 | 2019 |
| A deep learning framework for predicting protein functions with co-occurrence of GO terms M Li, W Shi, F Zhang, M Zeng, Y Li IEEE/ACM Transactions on Computational Biology and Bioinformatics 20 (2 …, 2022 | 6 | 2022 |
| HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins F Zhang, M Li, J Zhang, L Kurgan Nucleic Acids Research 51 (5), e25-e25, 2023 | 4 | 2023 |
| DeepPRObind: Modular deep learner that accurately predicts structure and disorder-annotated protein binding residues F Zhang, M Li, J Zhang, W Shi, L Kurgan Journal of Molecular Biology 435 (14), 167945, 2023 | 2 | 2023 |
| A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches W Wang, Y Shuai, Q Yang, F Zhang, M Zeng, M Li Briefings in Bioinformatics 25 (2), bbae050, 2024 | | 2024 |
| Machine learning methods for predicting protein-nucleic acids interactions M Li, F Zhang, L Kurgan Machine Learning in Bioinformatics of Protein Sequences: Algorithms …, 2023 | | 2023 |
| Supplementary Materials for “HybridRNAbind: Prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins” F Zhang, M Li, J Zhang, L Kurgan | | |
Min LiSchool of Computer Science and Engineering, Central South University在 mail.csu.edu.cn 的电子邮件经过验证
