| Matrix factorization-based data fusion for the prediction of lncRNA–disease associations G Fu, J Wang, C Domeniconi, G Yu Bioinformatics 34 (9), 1529-1537, 2018 | 145 | 2018 |
| Protein–protein interactions prediction based on ensemble deep neural networks L Zhang, G Yu, D Xia, J Wang Neurocomputing 324, 10-19, 2019 | 107 | 2019 |
| Semi-supervised classification based on random subspace dimensionality reduction G Yu, G Zhang, C Domeniconi, Z Yu, J You Pattern Recognition 45 (3), 1119-1135, 2012 | 97 | 2012 |
| Incomplete multi-view weak-label learning. Q Tan, G Yu, C Domeniconi, J Wang, Z Zhang Ijcai, 2703-2709, 2018 | 75 | 2018 |
| Protein function prediction using multilabel ensemble classification G Yu, H Rangwala, C Domeniconi, G Zhang, Z Yu IEEE/ACM Transactions on Computational Biology and Bioinformatics 10 (4 …, 2013 | 75 | 2013 |
| Transductive multi-label ensemble classification for protein function prediction G Yu, C Domeniconi, H Rangwala, G Zhang, Z Yu Proceedings of the 18th ACM SIGKDD international conference on Knowledge …, 2012 | 69 | 2012 |
| BRWLDA: bi-random walks for predicting lncRNA-disease associations G Yu, G Fu, C Lu, Y Ren, J Wang Oncotarget 8 (36), 60429, 2017 | 62 | 2017 |
| A literature review of gene function prediction by modeling gene ontology Y Zhao, J Wang, J Chen, X Zhang, M Guo, G Yu Frontiers in genetics 11, 400, 2020 | 61 | 2020 |
| Feature-induced partial multi-label learning G Yu, X Chen, C Domeniconi, J Wang, Z Li, Z Zhang, X Wu 2018 IEEE International Conference on Data Mining (ICDM), 1398-1403, 2018 | 61 | 2018 |
| Weighted-object ensemble clustering: methods and analysis Y Ren, C Domeniconi, G Zhang, G Yu Knowledge and Information Systems 51, 661-689, 2017 | 57 | 2017 |
| Activehne: Active heterogeneous network embedding X Chen, G Yu, J Wang, C Domeniconi, Z Li, X Zhang IJCAI, 2019 | 55 | 2019 |
| Ranking-based deep cross-modal hashing X Liu, G Yu, C Domeniconi, J Wang, Y Ren, M Guo Proceedings of the AAAI conference on artificial intelligence 33 (01), 4400-4407, 2019 | 53 | 2019 |
| Multi-label co-training Y Xing, G Yu, C Domeniconi, J Wang, Z Zhang Proceedings of the 27th international joint conference on artificial …, 2018 | 52 | 2018 |
| Hybrid cluster ensemble framework based on the random combination of data transformation operators Z Yu, HS Wong, J You, G Yu, G Han Pattern Recognition 45 (5), 1826-1837, 2012 | 52 | 2012 |
| Clustering cancer gene expression data by projective clustering ensemble X Yu, G Yu, J Wang PloS one 12 (2), e0171429, 2017 | 50 | 2017 |
| Predicting protein functions using incomplete hierarchical labels G Yu, H Zhu, C Domeniconi BMC Bioinformatics 16 (1), 2015 | 48 | 2015 |
| Multi-view multiple clusterings using deep matrix factorization S Wei, J Wang, G Yu, C Domeniconi, X Zhang Proceedings of the AAAI conference on artificial intelligence 34 (04), 6348-6355, 2020 | 45 | 2020 |
| BMC3C: binning metagenomic contigs using codon usage, sequence composition and read coverage G Yu, Y Jiang, J Wang, H Zhang, H Luo Bioinformatics 34 (24), 4172-4179, 2018 | 43 | 2018 |
| Predicting protein function via semantic integration of multiple networks G Yu, G Fu, J Wang, H Zhu IEEE/ACM transactions on computational biology and bioinformatics 13 (2 …, 2015 | 43 | 2015 |
| Semi-supervised ensemble classification in subspaces G Yu, G Zhang, Z Yu, C Domeniconi, J You, G Han Applied Soft Computing 12 (5), 1511-1522, 2012 | 43 | 2012 |
Jun Wang（王峻）Professor, School of Software, Shandong UniversityVerified email at sdu.edu.cn
