Keynote Speaker

Invited Speakers

     Yong Zhang, Ph.D.

Professor of Bioinformatics School of Life Science and Technology, Tongji University , China
(Web) http://www.tongji.edu.cn/~zhanglab

    Dr. Yong Zhang is the developer of a tool named MACS, which is prevalent as the peak-caller of the ChIP-Seq data. His research interest focuses on the design of the statistical methods,  computational algorithms and subsequent analysis on high-throughput data. Biologically, he especially studies epigenetic priming effects on stem cell differentiation. He is currently the professor of Tongji University and his laboratory performs many collaboration with data-generating labs. He won a number of honors and awards: Shanghai Science and Technology Rising Star (2010); New Century Excellent Talents in University (2011); Shanghai Science and Technology Excellent Rising Star (2013); National Natural Science Foundation for Excellent Young Scholars (2013); National Program for Support of Top-notch Young Professionals (2015).

Representative Publications

  1. Zhang Y*, Liu T*, Meyer CA, Eeckhoute J, Johnson DJ, Myers RM, Bernstein BE, Nussbaum C, Brown M, Li W, Liu XS. Model-based analysis of ChIP-Seq (MACS).Genome Biol 2008; 9:R137.
  2. Vastenhouw NL*, Zhang Y*, Woods IG, Imam F, Regev A, X. Liu XS, Rinn J, Schier A. Chromatin signature of embryonic pluripotency is established during zygotic genome activation. Nature 2010;464:922-6.
  3. Feng J, Meyer CA, Wang Q, Liu JS, Liu XS$, Zhang Y$. GFOLD: a generalized fold change for ranking differentially ex-pressed genes from RNA-seq data. Bioinformatics 2012; 28(21):2782-8.

 

   Shuhua Xu, Ph.D.

Professor of CAS-MPG Partner Institute for Computational Biology (PICB) / Shanghai Institutes for Biological Sciences (SIBS)/ Chinese Academy of Sciences (CAS)/ Distinguished Adjunct Professor, ShanghaiTech University, China
(Web) http://www.picb.ac.cn

          Dr. Shuhua Xu is the human population genetics professor at CAS-MPG Partner Institute for Computational Biology. He has focused on population genomics research of human admixture history and biological adaptation to the local environment. His major work was involved in the GWAS of Han Chinese, and he showed that the most differentiated genes among them was cardiac arteriopathy [1]. Furthermore, he discovered that the most differentiated variants were related to hypoxia-inducible factors in the GWAS of Tibetans and Han Chinese [2]. He also developed a software, PEAS, analysing SNPs for population genetics and molecular phylogenetics studies [3].

Representative Publications

  1. Shuhua Xu, Xianyong Yin, Shilin Li, Wenfei Jin, Haiyi Lou, Ling Yang, Xiaohong Gong, Hongyan Wang, Yiping shen, Xuedong Pan, Yungang He, Yajun Yang, Yi Wang, Wenqing Fu, Yu An, Jiucun Wang, Jingze Tan, Ji Qian, Xiaoli Chen, Xin Zhang, Yangfei Sun, Xuejun Zhang, Bailin Wu, Li Jin. 2009. Genomic Dissection of Population Substructure of Han Chinese and Its Implication in Association Studies. Am.J.Hum.Genet. 85:762-774.
  2. Shuhua Xu*, Shilin Li, Yajun Yang, Jingze Tan, Haiyi Lou, Wenfei Jin, Ling Yang, Xuedong Pan, Jiucun Wang, Yiping Shen, Bailin Wu, Hongyan Wang, Li Jin*. 2011. A Genome-Wide Search for Signals of High Altitude Adaptation in Tibetans. Mol.Biol.Evol. 28: 1003-1011.
  3. Shuhua Xu*, Sanchit Guputa and Li Jin*. 2010. PEAS V1.0: A Package for Elementary Analysis of SNP Data. Mol.Ecol.Res. 10:1085-1088.

 

  Xing-Ming Zhao, Ph.D.

Professor of Department of Computer Science
School of Electronics and Information Engineering, China
Tongji University, China
(Web) http://www.comp-sysbio.org    

         Dr. Xing-Ming Zhao is a Professor at Tongji University since 2012. He develops algorithms, statistical approaches and mathematical models to apply to system biology and neural informatics. His research interests is analysis of molecular interaction networks, identification of signaling pathway, prediction of drug-protein interactions and drug combinations / repositioning.

Representative Publications

  1. Zhao, X.M., Wang, R.S., Chen, L. and Aihara, K., 2008. Uncovering signal transduction networks from high-throughput data by integer linear programming. Nucleic acids research, 36(9), pp.e48-e48.
  2. Zhang, X., Zhao, X.M., He, K., Lu, L., Cao, Y., Liu, J., Hao, J.K., Liu, Z.P. and Chen, L., 2012. Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information.Bioinformatics, 28(1), pp.98-104.
  3. Zhang, X., D., Song, J., Bork, P. and Zhao, X.M., 2016. The exploration of network motifs as potential drug targets from post-translational regulatory networks. Scientific reports, 6.