| Incorporating distance-based top-n-gram and random forest to identify electron transport proteins X Ru, L Li, Q Zou Journal of Proteome Research 18 (7), 2931-2939, 2019 | 82 | 2019 |
| NerLTR-DTA: drug–target binding affinity prediction based on neighbor relationship and learning to rank X Ru, X Ye, T Sakurai, Q Zou Bioinformatics 38 (7), 1964-1971, 2022 | 42 | 2022 |
| Selecting essential MicroRNAs using a novel voting method X Ru, P Cao, L Li, Q Zou Molecular Therapy-Nucleic Acids 18, 16-23, 2019 | 38 | 2019 |
| Application of machine learning for drug–target interaction prediction L Xu, X Ru, R Song Frontiers in genetics 12, 680117, 2021 | 37 | 2021 |
| Exploration of the correlation between GPCRs and drugs based on a learning to rank algorithm X Ru, L Wang, L Li, H Ding, X Ye, Q Zou Computers in Biology and Medicine 119, 103660, 2020 | 28 | 2020 |
| Current status and future prospects of drug–target interaction prediction X Ru, X Ye, T Sakurai, Q Zou, L Xu, C Lin Briefings in Functional Genomics 20 (5), 312-322, 2021 | 17 | 2021 |
| Application of learning to rank in bioinformatics tasks X Ru, X Ye, T Sakurai, Q Zou Briefings in bioinformatics 22 (5), bbaa394, 2021 | 12 | 2021 |
| Identification of phage viral proteins with hybrid sequence features X Ru, L Li, C Wang Frontiers in Microbiology 10, 507, 2019 | 12 | 2019 |
| Optimization of drug–target affinity prediction methods through feature processing schemes X Ru, Q Zou, C Lin Bioinformatics 39 (11), btad615, 2023 | 7 | 2023 |
| Research on drug–target binding affinity prediction based on machine learning methods X Ru | | 2023 |
