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Publications

Selected Papers

  • Hum-mPLoc 3.0: Prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features. Hang Zhou, Yang Yang, and Hong-Bin Shen. Bioinformatics, 33(6), 2017, 843-853

  • MiRFFS: a functional group-based feature selection method for the identification of microRNA biomarkers. Yang Yang, Yiqun Xiao, Tianyu Cao and Wei Kong. Int. J. Data Mining and Bioinformatics, vol. 18(1), 2017

  • Improving clustering of microRNA microarray data by incorporating functional similarity. Yang Yang, Zhichen Wu and Wei Kong, Current Bioinformatics,2016,11(999)

  • A clustering-based approach for the identification of microRNA combinatorial biomarkers. Yang Yang, Ning Huang, Luning Hao and Wei Kong. BMC Genomics, 2017, 18(s2)

  • Kaiwen Liu, Yang Yang*,“Incorporating link information in feature selection for identifying tumor biomarkers by using miRNA-mRNA paired expression data”, Current Proteomics, 2017

  • Differences of immune disorders between Alzheimer's disease and breast cancer based on transcriptional regulation. Wei Kong, Xiaoyang Mou, Jin Deng, Benteng Di, Ruxing Zhong, Shuaiqun Wang, Yang Yang, Weiming Zeng. Plos One, 12(7):e0180337

  • Missing value imputation for microRNA expression data by using a GO-based similarity measure. Yang Yang, Zhuangdi Xu and Dandan Song. BMC bioinformatics, 2016, 17(1):109

  • Improving clustering of microRNA microarray data by incorporating functional similarity. Yang Yang, Zhichen Wu and Wei Kong. Current bioinformatics, 2016

  • The Construction of Common and Specific Significance Subnetworks of Alzheimer’s Disease from Multiple Brain Regions. Wei Kong, Xiaoyang Mou, Na Zhang, Weiming Zeng, Shasha Li and Yang Yang. BioMed research international, 2015

  • Roles of small RNAs in soybean defense against Phytophthora sojae infection. James Wong, Lei Gao, Yang Yang et al. The Plant Journal, 2014, 79(6):928–940

  • A new feature selection method for computational prediction of type III secreted effectors. Yang Yang and Sihui Qi. International journal of data mining and bioinformatics, 2014, 10(4):440–454

  • Identification of novel type III effectors using latent Dirichlet allocation. Yang, Yang. Computational and mathematical methods in medicine, 2012, 2012():

  • Learning from imbalanced data sets with a Min-Max modular support vector machine. Bao-Liang Lu, Xiao-Lin Wang, Yang Yang and Hai Zhao. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(1):56–71

  • Computational prediction of type III secreted proteins from gram-negative bacteria. Yang Yang, Jiayuan Zhao, Robyn L Morgan, Wenbo Ma and Tao Jiang. BMC bioinformatics, 2010, 11(1):1

  • Protein subcellular multi-localization prediction using a min-max modular support vector machine. Yang Yang and Bao-Liang Lu. International Journal of Neural Systems, 2010, 20(01):13–28

  • Computational prediction of novel non-coding RNAs in Arabidopsis thaliana. Dandan Song, Yang Yang, Bin Yu, Binglian Zheng, Zhidong Deng, Bao-Liang Lu, Xuemei Chen and Tao Jiang. BMC bioinformatics, 2009, 10(1):1

  • Feature reduction using a topic model for the prediction of type iii secreted effectors. Sihui Qi, Yang Yang and Anjun Song. International Conference on Neural Information Processing, pp.155–163 ( 2011)