Optimization and Computational Systems Biology Lab
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Yong Wang

Professor
Institute of Applied Methematics
Academy of Methematics and Systems Science(AMSS)
Chinese Academy of Sciences(CAS)
No. 55 Zhongguacun East Road, Beijing, China

Email: ywang@amss.ac.cn
Office: Siyuan Building, Room 624
Phone: +86-10-82541372 (O), +86-13693300386 (C)

BIOGRAPHY

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, in the Center for Excellence in Animal Evolution and Genetics (CEAEG) at Chinese Academy of Sciences, in the School of Mathematics, University of Chinese Academy of Sciences at Chinese Academy of Sciences, and in the Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences 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). .

Here is my Google Scholar profile.

SELECTED PAPERS

  1. Time course regulatory analysis based on paired expression and chromatin accessibility data. Genome Research. vol. 30: 622-634 (2020).
  2. 3Scover: Identifying safeguard TF from cell type-TF specificity network by an extended minimum set cover model. iScience. vol. 23(6): 101227. (2020).
  3. Integrating distal and proximal information to predict gene expression via a densely con-nected convolutional neural network. Bioinformatics. vol. 36, Issue 2, P496-503 (2020).
  4. Associating lncRNAs with small molecules via bilevel optimization reveals cancer-related lncRNAs. PLoS Computational Biology. vol. 15 (12), e1007540 (2019).
  5. Lipid-gene regulatory network reveals coregulations of triacylglycerol with phosphatidylinositol/lysophosphatidylinositol and with hexosyl-ceramide. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids. vol. 1864, Issue 2, Pages 168-180 (2019).
  6. scTIM: Seeking Cell-Type-Indicative Marker from single cell RNA-seq data by consensus optimization. Bioinformatics. vol. 36, Issue 8, P2474ĘC2485, (2019).
  7. DC3 is a method for deconvolution and coupled clustering from bulk and single-cell genomics data. Nature communications. vol. 10 (1), 1-11 (2019).
  8. Hierarchical graphical model reveals HFR1 bridging circadian rhythm and flower development in Arabidopsis thaliana. npj Systems Biology and Applications. vol. 5(1), 1-11 (2019).
  9. TFAP2C- and p63-Dependent Networks Sequentially Rearrange Chromatin Landscapes to Drive Human Epidermal Lineage Commitment. Cell Stem Cell. Vol. 24, Issue 2, P271-284.E8, (2019).
  10. Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations. Proceedings of the National Academy of Sciences. 115 (30) 7723-7728 (2018).
  11. Modeling gene regulation from paired expression and chromatin accessibility data. Proceedings of the National Academy of Sciences. vol. 114 no. 25 E4914-E4923 (2017).
  12. A systematic method to identify modulation of transcriptional regulation via chromatin activity reveals regulatory network during mESC differentiation. Scientific Report 6, 22656 (2016).
  13. 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).
  14. Computational probing protein-protein interactions targeting small molecules. Bioinformatics. 32 (2), 226-234 (2015).
  15. A novel mixed integer programming for multi-biomarker panel identification by distinguishing malignant from benign colorectal tumors. Methods. 83: 3-17 (2015).
  16. Drug repositioning by kernel-based integration of molecular structure, molecular activity, and phenotype data. PLoS One 8 (11), e78518 (2013).
  17. Network predicting drug's anatomical therapeutic chemical code. Bioinformatics 29 (10), 1317-1324 (2013).
  18. ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions. Nucleic Acids Research 41 (4), e53-e53 (2012).
  19. Revealing metabolite biomarkers for acupuncture treatment by linear programming based feature selection. BMC Systems Biology. 6 (1), S15 (2012)
  20. A combinatorial model and algorithm for globally searching community structure in complex networks. Journal of Combinatorial Optimization. 23 (4), 425-442 (2012).
  21. 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).
  22. Protein evolution in yeast transcription factor subnetworks. Nucleic Acids Res. 38: 5959-5969 (2010).
  23. Predicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide data. Nucleic Acids Res. 37: 5943-5958 (2009).
  24. Evaluating protein similarity from coarse structures. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 6 (4), 583-593 (2009).
  25. A network biology study on circadian rhythm by integrating various omics data. OMICS A Journal of Integrative Biology. 13 (4), 313-324 (2009).
  26. Condition specific subnetwork identification using an optimization model. Lecture Notes in Operations Research. 9, 333-340 (2008).
  27. Analysis on multi-domain cooperation for predicting protein-protein interactions. BMC bioinformatics. 8 (1), 391 (2008).
  28. Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics. 22 (19), 2413-2420 (2006).
  29. Exploring protein's optimal HP configurations by self-organizing mapping. Journal of bioinformatics and computational biology. 3 (02), 385-400 (2005).
  30. A new trust region method for nonlinear equations. Mathematical Methods of Operations Research. 58 (2), 283-298 (2003).
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Yong Wang Lab  |  Academy of Mathematics and Systems Science  |  Chinese Academy of Sciences