Email: yangyang AT cs DOT sjtu DOT edu DOT cn
Office: 3#-501 of SEIEE buildings in Minghang campus
She is interested in developing efficient algorithms and software for computational problems in molecular biology and biomedine with Machine Learning and Data Ming approaches. In particular, her research includes biological sequence analysis and classification, non-coding RNA identification and gene regulatory network construction for complex diseases.
Ph.D. Computer Science (June, 2009), Shanghai Jiao Tong University, Shanghai, P. R. China
Visiting Ph.D. student (2007 - 2009), University of California, Riverside, CA, USA
B.S. Computer Science (June, 2003), Shanghai Jiao Tong University, Shanghai, P. R. China
Assistant Professor, Dept. of Computer Science, Shanghai Jiao Tong Univ., Shanghai, Feb 2014 -
Research Associate, Dept. of Computer Science, University of California, Riverside, Oct 2012 - Oct 2013
Associate Professor, Dept. of Computer Science, Shanghai Maritime Univ., Shanghai, Jul 2012 - Jan 2014
Assistant Professor, Dept. of Computer Science, Shanghai Maritime Univ., Shanghai, Jul 2009 - Jun 2012
· Algorithms (EI334)
l “Computational prediction of type III secreted effectors from gram-negative bacteria”, the National Natural Science Foundation of China (Grant No. 61003093), USD 32K, 1/1/2011 - 12/31/2013.
l “Large scale protein sequence classification based on machine learning methods”, the Science & Technology Program of Shanghai Maritime University (Grant No. 20110009), USD 17K, 1/1/2011 - 12/31/2012.
l “Study on the classification of biological sequences”, the Science Foundation for The Excellent Youth Scholars of Shanghai Municipality, USD 5K, 7/1 /2010 - 6/30/2012.
1. Yang Yang and Sihui Qi, “A new feature selection method for computational prediction of type III secreted effectors”, Vol. 10, No. 4, International Journal of Data Mining and Bioinformatics, 2014
2. Kong, Wei, Jingmao Zhang, Xiaoyang Mou, and Yang Yang. "Integrating Gene Expression and Protein Interaction Data for Signaling Pathway Prediction of Alzheimer’s Disease." Computational and mathematical methods in medicine, 2014
3. Tingting Sui, Yang Yang, and Xiaofeng Wang, "Sequence-based feature extraction for type III effector prediction", International Journal of Bioscience, Biochemistry and Bioinformatics, Vol.3(3), 2013
4. Yang Yang, "Identification of novel type III effectors using latent Dirichlet allocation," Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 696190
5. Bao-liang Lu, Xiaolin Wang, Yang Yang and Hai Zhao, “Learning from imbalanced data sets with a Min-Max modular support vector machine,” Frontiers of Electrical and Electronic Engineering, vol. 6(1), pp. 56-71, 2011
6. Yang Yang, Jiayuan Zhao, Robyn L. Morgan, Wenbo Ma, Tao Jiang, “Computational prediction of type III secreted proteins from gram-negative bacteria,” BMC Bioinformatics, 2010, 11(S1):S47
7. Yang Yang and Bao-Liang Lu, “Protein subcellular multi-localization prediction using a min-max modular support vector machine,” International Journal of Neural Systems, vol. 20, No. 1, pp. 13-28, 2010.
8. Yang Yang, “Computational prediction of type III secreted proteins using labeled and unlabeled data,” International Journal of Infectious Diseases, vol. 14, suppl.2, pp. 37, 2010.
9. Dandan Song*, Yang Yang* (equal contribution), Bin Yu, Binglian Zheng, Zhidong Deng, Bao-Liang Lu, Xuemei Chen and Tao Jiang, “Computational prediction of novel non-coding RNAs in Arabidopsis thaliana”, BMC Bioinformatics 2009, 10(S1):S36
10. Yimin Wen, Yang Yang and Bao-Liang Lu, “A study on the application of ensemble learning algorithms in incremental learning”, Journal of Computer Research and Development (in Chinese) 2005, 42, 222-227
11. Yang Yang, James Wong and Wenbo Ma, “Identification of soybean microRNAs involved in Phytophthora sojae infection by deep sequencing”, poster, The Twelfth Asia Pacific Bioinformatics Conference (APBC 2014)
12. Sihui Qi, Yang Yang and Anjun Song, “Feature reduction using a topic model for the prediction of type III secreted effectors,” Neural Information Processing, Lecture Notes in Computer Science vol.7062, pp. 155-163 (ICONIP 2011)
13. Yuelan Zhang, Yang Yang and Xiaofeng Wang, “Extracting features from protein sequences for the prediction of type III secreted effector,” Proc. of the 6th International Conference on Bioinformatics and Biomedical Engineering , pp. 23 – 26 (iCBBE 2011)
14. Yang Yang, “A comparative study on sequence feature extraction for type III secreted effector prediction,” Proc. of the 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 1560 - 1564 (FSKD 2011)
15. Yang Yang, “A new similarity measure over Gene Ontology with application to protein subcellular localization,” Proc. of the 3rd International Conference on Biomedical Engineering and Informatics, vol. 6, pp.2452-2456. (BMEI 2010)
16. Yang Yang, Bao-liang Lu and Wen-Yun Yang, ``Classification of protein sequences based on word segmentation methods,'' Proc. of the 6th Asia Pacific Bioinformatics Conference (APBC 2008), Kyoto, Japan, pp. 177-186.
17. Yang Yang and Bao-Liang Lu, ``Incorporating domain knowledge into a min-max modular support vector machine for protein subcellular localization,'' Neural Information Processing, - ICONIP 2007, LNCS, vol. 4985, pp. 827-836, 2008
18. Wenyun Yang, Bao-Liang Lu and Yang Yang, “A Comparative study on feature extraction from protein sequences for subcellular localization prediction,” Proc. of 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2006), Toronto, Ontario, Canada, pp. 201-208.
19. Yang Yang and Bao-Liang Lu, “Prediction of protein subcellular multi-locations with a min-max modular support vector machine,” Advances in neural networks – ISNN 2006, LNCS, vol.3973, pp.667-673, 2006.
20. Yang Yang and Bao-Liang Lu, “Extracting features from protein sequences using Chinese segmentation techniques for subcellular localization,” Proc. of 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2005), San Diego, California, USA, pp 288-295.
21. Yang Yang and Bao-Liang Lu, “Structure pruning strategies for min-max modular network”, Advances in neural networks – ISNN 2005, LNCS, vol. 3496, pp. 646-651, 2005
l Member of ISCB - International Society for Computational Biology
l Program committee member of 2010 International Symposium on Neural Networks
l Program committee member of 2011 International Conference on Neural Information Processing
l Deputy Secretary-general for the Noetic Science Association of Shanghai
ACC-SSE: A tool for the prediction of type III secreted effector proteins using the information of solvent accessibility and second structural elements [Source code]
SubLoc Predictor: A tool for the prediction of protein subcellular localization[Source code]