个人简介
I am a Professor in the Institute of Applied Mathematics at Academy of Mathematics and Systems Science at at Chinese Academy of Sciences (CAS), where I am also affiliated as a Professor in the National Center for Mathematics and Interdisciplinary Sciences (NCMIS) at Chinese Academy of Sciences.
I graduated from Inner Mongolia University with B.S. in Mathematics and Physics (1995-1999). I obtained my M.S. degree from the Dalian University of Science and Technology in Operations Research and Control Theory (1999-2002). I received my Ph.D. in Operations Research and Control Theory from Academy of Mathematics and Systems Science at at Chinese Academy of Sciences (CAS) (2002-2005). My Ph.D thesis was specializing in optimization model and algorithm for protein structure prediction and classification with Prof. Xiang-Sun Zhang. I carried out postdoctoral research in E-government strategy planning with Prof. Chun-Zheng Wang at the State Information Center in China and later in bioinformatics with Prof. Luonan Chen at the Department of Electronics Information and Communications, Osaka Sangyo University in Japan (2005-2007). After joining Academy of Mathematics and Systems Science, I visited the Bioinformatics Program in Boston University as a research associate (2007-2008), the Computational Biology Research Center (CBRC) of National Institute of Advanced Industrial Science and Technology (AIST) in Japan as a research scientist (2010-2011), Department of Statistics, Bio-X program, and The Center for Personal Dynamic Regulome in Center of Excellence in Genomic Science (CEGS) in Stanford University as research associate (2013-2016).
研究领域
Our research focuses on optimization, computational biology, and systems biology. We aim to construct networks for complex biomolecular systems such as gene regulatory networks via optimization and statistics models. By further integrating multiple data sources into network models, we aim to elucidate the relationship between sequence variant, regulatory element, 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.
Gene Regulatory Network Modeling. : modeling and analysis of gene regulatory network. Ongoing projects in the lab include: interactions among chromatin regulators, sequence specific transcription factors and cis-regulatory sequence elements; context specific regulatory network reconstruction.
Computational Systems Biology. : bridging the phenotype and genotype by network modeling. Ongoing projects in the lab include: reconstruction of network models and mechanism underlying complex traits such as development, differentiation, reprogramming, and evolutionary adaption by genomic data integration; revealing regulatory elements, chromatin regulators, transcriptional factors, and genes and their function and dynamics in biological processes.
Data Integration and Modeling. : data integration and representation modeling. Ongoing projects in the lab include: heterogeneous and multi-layer data integration methodology; matched genomic data integration; complex network data exploration; data dimensional reduction models to reveal key molecules.
Optimization and Statistics Models. : developing novel optimization and statistical methods to analyze biological sequence variant, regulatory element, regulator, gene expression, evolution, function, phenotype data of interest. We are also interested in the nonlinear combinatorial optimization and connection between deterministic optimization and statistical models.
近期论文
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Modeling gene regulation from paired expression and chromatin accessibility data. Proceedings of the National Academy of Sciences. vol. 114 no. 25 E4914-E4923 (2017).
A systematic method to identify modulation of transcriptional regulation via chromatin activity reveals regulatory network during mESC differentiation. Scientific Report 6, 22656 (2016).
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).
Computational probing protein-protein interactions targeting small molecules. Bioinformatics. 32 (2), 226-234 (2015).
A novel mixed integer programming for multi-biomarker panel identification by distinguishing malignant from benign colorectal tumors. Methods. 83: 3-17 (2015).
Drug repositioning by kernel-based integration of molecular structure, molecular activity, and phenotype data. PLoS One 8 (11), e78518 (2013).
Network predicting drug's anatomical therapeutic chemical code. Bioinformatics 29 (10), 1317-1324 (2013).
ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions. Nucleic Acids Research 41 (4), e53-e53 (2012).
Revealing metabolite biomarkers for acupuncture treatment by linear programming based feature selection. BMC Systems Biology. 6 (1), S15 (2012)
A combinatorial model and algorithm for globally searching community structure in complex networks. Journal of Combinatorial Optimization. 23 (4), 425-442 (2012).
A linear programming framework for inferring gene regulatory networks by integrating heterogeneous data. Handbook of Research on Computational Methodologies in Gene Regulatory Networks 450-475 (2010).
Protein evolution in yeast transcription factor subnetworks. Nucleic Acids Res. 38: 5959-5969 (2010).
Predicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide data. Nucleic Acids Res. 37: 5943-5958 (2009).
Evaluating protein similarity from coarse structures. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 6 (4), 583-593 (2009).
A network biology study on circadian rhythm by integrating various omics data. OMICS A Journal of Integrative Biology. 13 (4), 313-324 (2009).
Condition specific subnetwork identification using an optimization model. Lecture Notes in Operations Research. 9, 333-340 (2008).
Analysis on multi-domain cooperation for predicting protein-protein interactions. BMC bioinformatics. 8 (1), 391 (2008).
Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics. 22 (19), 2413-2420 (2006).
Exploring protein's optimal HP configurations by self-organizing mapping. Journal of bioinformatics and computational biology. 3 (02), 385-400 (2005).
A new trust region method for nonlinear equations. Mathematical Methods of Operations Research. 58 (2), 283-298 (2003).
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