演讲嘉宾--韩敬东

韩敬东

jackie.jdhan@gmail.com
http://www.picb.ac.cn/hanlab/
Title: Professor, Director
Institute: CAS-MPG Partner Institute for Computational Biology http://www.picb.ac.cn/hanlab
Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences 320 Yue Yang Road, Shanghai, 200031, China

研究学习经历:

Prof. Jing-Dong Jackie Han obtained Ph.D. degree from Albert Einstein College of Medicine. She had her postdoctoral training at The Rockefeller University and Dana-Farber Cancer Institute. In 2004, she became an investigator/professor at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. She is currently a director of the CAS-Max Planck Partner Institute for Computational Biology. Her research focuses on the structure and dynamic inference of molecular networks,using a combination of large-scale experiments and computational analysis to probe the networks and to integrate functional interaction data in order to explore the design principles of the networks and to find how the complex phenotypes, such as aging, cancer and stem cell development are regulated through molecular networks. She was awarded the Chinese Academy Sciences Hundred Talent Plan and NSFC Outstanding Young Scientist Award in 2006, and the Hundred Talent Plan Outstanding Achievement Award in 2009, selected as a Max Planck Follow in 2011 and a MaxNetAging Fellow in 2014.

Selected Publications:
  1. Lei Hou^, Dan Wang^, Di Chen^, Yi Liu, Yue Zhang, Hao Cheng, Chi Xu, Na Sun, Joseph McDermott, and William B. Mair and Jing-Dong J. Han*. A Systems Approach to Reverse Engineer Lifespan Extension by Dietary Restriction. Cell Metabolism, 2016, 8;23(3):529-40.
  2. Weiyang Chen1, Wei Qian1, Gang Wu1, Weizhong Chen1, Bo Xian1, Xingwei Chen1, Yaqiang Cao1, Christopher D Green1, Fanghong Zhao2, Kun Tang1 and Jing-Dong J. Han1,*,Three-dimensional human facial morphologies as robust aging markers, Cell Research, 2015, 25:574-587 (highlighted by Nature Press Release, Science News, New Scientists, The Guardian, Daily Mail, and NewsWeek, etc).
  3. Weizhong Chen1,2†, Yi Liu1,3†, Shanshan Zhu1, Christopher D. Green1, Gang Wei1, Jing-Dong J. Han1,*, Improved Nucleosome Positioning Algorithm iNPS for Accurate Nucleosome Positioning from Sequencing Data, Nature Communications 2014, doi:10.1038/ncomms5909.
  4. Ming Su^, Dali Han^, Jerome D Boyd-Kirkup, Xiaoming Yu and Jing-Dong J Han*Evolution of Alu towards enhancers, Cell Reports, 2014, 7(2): 376-385
  5. Jin’e Li^, Yi Liu^, Min Liu and Jing-Dong J Han*Functional dissection of regulatory models using gene expression data of deletion mutants, PLoS Genetics, 2013, 9(9): e1003757.
  6. Wei Zhang^, Yi Liu^, Na Sun, Dan Wang, Jerome Boyd-Kirkup, Xiaoyang Dou, Jing-Dong J Han*, Integrating Genomic, Epigenomic and Transcriptomic Features Reveals Modular Signatures Underlying Poor Prognosis in Ovarian Cancer, Cell Reports, 2013, 4(3):542–553
会议报告摘要:
Integrative Analysis of Biological Big Data (4月22日,09.50-10.20am)

New high-throughput technologies, such as microarrays and deep sequencing technologies, have provided unprecedented opportunities for mapping mutations, transcripts, transcription factor binding and histone modifications at high resolution and at genome-wide level. This has revolutionized the way regulations of diseases and other biological processes are studies and generated a large amount of heterogeneous data, which is begging to be unbiasedly and efficiently integrated. How to integrate these data still remains a big challenge. We have explored to ab initio predict or reconstruct regulatory networks based on heterogeneous data on gene expression, histone modification and genomic changes. We find that innovative integrations of these data can lead to not only global pictures of the complex biological processes, such as aging and early development, but also key regulatory events of these processes. We have also developed new computational algorithms to facilitate mapping of epigenetic features from the deep sequencing data. I will highlight our new methods and results for the integrative analyses of large datasets to infer regulatory events, in particular in light of incorporating the epigenome and imaging data recently generated by international consortiums.

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第四届肿瘤基础和转化医学前沿国际研讨会