主要从事运筹学与生物信息学、系统生物学的交叉研究，采用最优化方法和数学建模的方式，以生物分子网络和复杂生物数据集成为主线研究生物医学大数据的建模与分析。 

1.基因调控网络 

基因组学研究的主要目标是找出复杂性状和疾病的遗传变异（包括单核苷酸多态（SNP）和结构变异）。数量性状位点定位(quantitative trait locus, QTL)和全基因组关联分析(genome-wide association study, GWAS)等方法已经应用于分析复杂性状和复杂疾病的遗传结构，发现落在98%的非编码调控区域的变异，但这些非编码区域的位点怎么跟表型产生联系却非常棘手。其中一个根本困难是基因调控的组织、条件特异性。即在特定的条件下，特定基因表达的启动或停止，增强或抑制，是细胞选择基因组中的调控元件和相互作用完成基本生命活动以及对外界刺激作出应答的分子基础。而且组织和细胞特异的基因调控塑造了不同的表型，是健康和疾病研究的基石。我们拟建模复杂表型的发生发展过程的基因调控网络及其驱动下的演化过程，发展数学模型和计算方法分析、集成生物医学大数据，推断因果调控关系，从数据中深度挖掘信息和规律。 

2复杂生物数据的建模与集成 

研究生成高维、多层面、异源、高噪声的“组学”数据的生物分子网络，通过分析复杂的生物医学数据，提炼出数学理论和算法，解决生物医学中的关键问题。(1)重点研究生物分子网络建模，在数学上找出产生数据的模型，揭示因果关系。(2)重点研究生物医学数据分析集成，研究数据的基本数学结构，通过建模分离信号与噪声。 

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

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 .

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. 

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

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. 

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

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

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. 

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. 

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. 

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. 

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. 

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.  

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. 

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.