| Predicting diabetes mellitus with machine learning techniques Q Zou, K Qu, Y Luo, D Yin, Y Ju, H Tang Frontiers in genetics 9, 515, 2018 | 901 | 2018 |
| Biological functions of microRNAs: a review Y Huang, XJ Shen, Q Zou, SP Wang, SM Tang, GZ Zhang Journal of physiology and biochemistry, 1-11, 2011 | 792 | 2011 |
| 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 | 514 | 2019 |
| The Symbiodinium kawagutii genome illuminates dinoflagellate gene expression and coral symbiosis S Lin, S Cheng, B Song, X Zhong, X Lin, W Li, L Li, Y Zhang, H Zhang, Z Ji, ... Science 350 (6261), 691-694, 2015 | 482 | 2015 |
| A novel features ranking metric with application to scalable visual and bioinformatics data classification Q Zou, J Zeng, L Cao, R Ji Neurocomputing 173, 346-354, 2016 | 425 | 2016 |
| A comprehensive overview and evaluation of circular RNA detection tools X Zeng, W Lin, M Guo, Q Zou PLoS computational biology 13 (6), e1005420, 2017 | 375 | 2017 |
| webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study C Cao, J Wang, D Kwok, F Cui, Z Zhang, D Zhao, MJ Li, Q Zou Nucleic acids research 50 (D1), D1123-D1130, 2022 | 361 | 2022 |
| Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks X Zeng, X Zhang, Q Zou Briefings in bioinformatics 17 (2), 193-203, 2016 | 361 | 2016 |
| Tumor origin detection with tissue-specific miRNA and DNA methylation markers W Tang, S Wan, Z Yang, AE Teschendorff, Q Zou Bioinformatics 34 (3), 398-406, 2018 | 358 | 2018 |
| Distance-based support vector machine to predict DNA N6-methyladenine modification H Zhang, Q Zou, Y Ju, C Song, D Chen Current Bioinformatics 17 (5), 473-482, 2022 | 316 | 2022 |
| Finding the best classification threshold in imbalanced classification Q Zou, S Xie, Z Lin, M Wu, Y Ju Big Data Research 5, 2-8, 2016 | 298 | 2016 |
| Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources Y Liu, X Zeng, Z He, Q Zou IEEE/ACM transactions on computational biology and bioinformatics 14 (4 …, 2016 | 298 | 2016 |
| LibD3C: ensemble classifiers with a clustering and dynamic selection strategy C Lin, W Chen, C Qiu, Y Wu, S Krishnan, Q Zou Neurocomputing 123, 424-435, 2014 | 269 | 2014 |
| Similarity computation strategies in the microRNA-disease network: a survey Q Zou, J Li, L Song, X Zeng, G Wang Briefings in functional genomics 15 (1), 55-64, 2016 | 268 | 2016 |
| 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 | 265 | 2017 |
| Sequence clustering in bioinformatics: an empirical study Q Zou, G Lin, X Jiang, X Liu, X Zeng Briefings in bioinformatics 21 (1), 1-10, 2020 | 258 | 2020 |
| 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 | 251 | 2013 |
| 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 | 243 | 2019 |
| Prediction of potential disease-associated microRNAs using structural perturbation method X Zeng, L Liu, L Lü, Q Zou Bioinformatics 34 (14), 2425-2432, 2018 | 238 | 2018 |
| 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 | 233 | 2018 |
Leyi WEIProfessor, School of Software, Shandong University確認したメール アドレス: sdu.edu.cn