WANG Yong  Ph.D.  

Education background

Dr. WANG Yong got his bachelor degree of Mathematics and Physics in 1999 from Inner Mongolia University, M.S. degree of Operations Research in 2002 from Dalian University of Science and Technology, Ph.D. degree in Operational Research and Bioinformatics in 2005 from Academy of Mathematics and Systems Science, CAS. From 2005 to 2007, he had taken the postdoctoral position at the State Information Center in China and the Department of Electronics Information and Communications, Osaka Sangyo University in Japan.

Work experience

From 2007. 9 to 2008.9, he visited the Bioinformatics Program in Boston University as a research associate. From 2010. 10 to 2011. 4, he served as the Research Staff in The Computational Biology Research Center (CBRC) of National Institute of Advanced Industrial Science and Technology (AIST) in Japan. From 2012. 11 to 2016. 1, he is a visiting scholar Department of Statistics and Center of Excellent Genomic Center in Stanford University. Since 2017, he is Professor at the Academy of Mathematics and Systems Science (AMSS), National Center for Mathematics and Interdisciplinary Sciences (NCMIS) in Chinese Academy of Sciences.

Research field

His major research interests are gene regulation and computational systems biology.

Award

Dr. Wang was recipient of The Outstanding Research award from The Ministry of Education, Science, and Technology Development (2017) and The Significant Achievement Award from the Academy of Mathematics and Systems Science of Chinese Academy of Sciences (2008, 2016).

Project

His research was funded by National Science Foundation of China (NSFC), Chinese Academy of Sciences (CAS), and Ministry of Science and Technology of China (MOST). Some recent grants include: Excellent Young Scholars Research Grant supported by NSFC11422108 Mathematical modeling for bio-medical big data (2015-2017), Research Grant supported by NSFC 61671444 Modeling gene regulation from matched expression and chromatin accessibility data (2017-2020), and Innovative Group Grant supported by NSFC 61621003, Theory and application of network knowledge (2017-2022).

We are interested in elucidating the relationship between sequence variant, regulatory element, transcription regulator, chromatin regulator, gene expression, and evolution of biomolecular systems, to probe design principles of biological regulations and networks, and to investigate systems biology mechanisms of complex traits. To achieve these aims, we develop diverse computational methods ranging from theory, model, and algorithm.

1.     Gene Regulatory Network Modeling and analysis. We are studying when, where, and how much mRNA is produced from a particular gene at species, tissue, context, condition, and single cell levels. We are investigating interactions among chromatin regulators, sequence specific transcription factors and cis-regulatory sequence elements and reconstructing context specific regulatory network. Particularly we are interested to explore the following fundamental questions: Where are the regulatory elements? Where are the regulatory boundaries in 3D? Which gene(s) are regulated by a given element? Which transcription factors may act on the element? In which cellular contexts will an element be activated? How does an active element (&TFs) affect transcription? How does genetic variants (trans or cis) affect transcription? Furthermore, we are bridging the phenotype and genotype by reconstruction of network models and mechanism underlying complex traits such as development, differentiation, reprogramming, and evolutionary adaption. We aims to reveal regulatory elements, chromatin regulators, transcriptional factors, and genes and their function and dynamics in biological processes.

2.     Data Integration and Modeling. We are interested to develop novel method for data integration and representation. We study the heterogeneous and multi-layer data integration methodology. We perform matched genomic data integration; complex network data exploration; data dimensional reduction models to reveal key molecules. We are developing novel optimization and statistical methods to analyze biological sequence variant, regulatory element, regulator, gene expression, evolution, function, phenotype data of interest.

1.         Zhanying Feng, Yong Wang*. ELF: Extract Landmark Features by optimizing topology maintenance, redundancy, and specificity. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2018

2.         Zhana Duren, Xi Chen, Mahdi Zamanighomi, Wanwen Zeng, Ansuman T. Satpathy, Howard Y. Chang, Yong Wang, and Wing Hung Wong. Integrative analysis of single cell genomics data by coupled nonnegative matrix factorizations. Proceedings of the National Academy of Sciences, 115 (30) 7723-7728, 2018

3.         Z Duren, X Chen, R Jiang, Yong Wang*, WH Wong. Modeling gene regulation from paired expression and chromatin accessibility data. Proceedings of the National Academy of Sciences, 2017.

4.         Yong Wang*, Rui Jiang, WH Wong. Modeling the causal regulatory network by integrating chromatin accessibility and transcriptome data. National Science Review, nww025, 2016

5.         Duren Zhana, Yong Wang*. A systematic method to identify modulation of transcriptional regulation via chromatin activity reveals regulatory network during mESC differentiation. Scientific Report. 2016.

6.         Yongcui Wang, Nai-Yang Deng, Shi-Long Chen, and Yong Wang*. Computational probing protein-protein interactions targeting small molecules. Bioinformatics, 32: 226-234, 2016.

7.         Meng Zou, Peng-Jun Zhang, Xin-Yu Wen, Luonan Chen, Ya-Ping Tian, and Yong Wang*. Identifying multi-biomarker to distinguish malignant from benign colorectal tumours by a mixed integer programming. Methods, 2015.

8.         Meng Zou, Zhaoqi Liu, Xiang-Sun Zhang, Yong Wang*. NCC-AUC: an AUC optimization method to identify multi-biomarker panel for cancer prognosis from genomic and clinical data. Bioinformatics, 31 (20), 3330-3338, 2015.

9.         Xianwen Ren, Yong Wang, Xiang-Sun Zhang, and Qi Jin. iPcc: a novel feature extraction method for accurate disease class discovery and prediction. Nucleic Acids Research. 2013 Vol. 41, No. 4, e143.

10.     Xianwen Ren, Yong Wang, Luonan Chen, Xiang-Sun Zhang, and Qi Jin. ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions. Nucleic Acids Research. 2013 41(4): e53.

11.     Yong Wang*, Qiao-Feng Wu, Chen Chen, Ling-Yun Wu, Xian-Zhong Yan, Shu-Guang Yu, Xiang-Sun Zhang, and Fan-Rong Liang.  Revealing metabolite biomarkers for acupuncture treatment by linear programming based feature selection. BMC Systems Biology, 6(Suppl 1):S15, 2012.

12.     Yong Wang, Eric Franzosa, Xiang-Sun Zhang, and Yu Xia. Protein evolution in yeast transcription factor subnetworks. Nucleic Acids Research, 38(18): 5959–5969, 2010.

13.     Yong Wang, Xiang-Sun Zhang, and Yu Xia. Predicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide data. Nucleic Acids Research. 37:5943-5958, 2009. 

14.     Yong Wang, Xiang-Sun Zhang, and Luonan Chen. A network biology study on circadian rhythm by integrating various omics data. OMICS: A Journal of Integrative Biology, Vol. 13, No. 4, 2009.

15.     Yong Wang, Trupti Joshi, Xiang-Sun Zhang, Dong Xu, and Luonan Chen. Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics, Vol. 22, 2413-2420, 2006.