More specifically, research interests include:

Optimization, statistics and machine learning for data science

His group has a general interest in developing optimization, statistics and machine learning models and algorithms for those computational problems that arise from the biological data (e.g., single-cell transcriptomic data, 3D chromatin structure) and generic data science (e.g., multi-view data, image data, complex networks). They are particularly interested in computational and statistical methods for data representation and pattern discovery in diverse big data.

Cancer genomics

The first step for clinical diagnostics, prognostics, and targeted therapeutics of cancer is to comprehensively understand its molecular mechanisms. Large-scale cancer genomics projects are providing a large volume of data about genomic, epigenomic, and gene expression aberrations in multiple cancer types. They aim to combine data from different sources with computational techniques to discover the underlying combinatorial patterns and driver pathways underlying cancer as well as other diseases. They also aim to develop methods to integrate different data sources to classify tumor and predict the clinical outcome of patients based on the genotype and molecular features.

Computational epigenetics

Chromatin modifications have been comprehensively illustrated to play important roles in gene regulation and cell diversity in recent years. Given the rapid accumulation of genome-wide chromatin modification maps across multiple cell types, there is an urgent need for computational methods to analyze multiple maps to reveal combinatorial modification patterns and define functional DNA elements. They aim to develop computational methods to address these issues and answer specific biological questions.

Network biology and network science

Network science emerged as powerful tools for studying biological systems and complex diseases which summarizes biological systems as nodes and edges among them. The tasks of uncovering the organization of networks and utilizing them for the understanding of biology and disease complexity prompt rich and diverse interests. They aim to develop methods to understand the topological organization of networks and reveal molecular characteristics by combing network structure and biological features.

1.  Chen J, Zhang S*. Discovery of two-level modular organization from matched genomic data via joint matrix tri-factorization. Nucleic Acids Research (2018), 46(12): 5967–5976.

2.  Cao Z, Zhang S*. Simple tricks of convolutional neural network architectures improve DNA–protein binding prediction. Bioinformatics (2018), bty893.

3.  Min W, Liu J*, Zhang S*. Edge-group sparse PCA for network-guided high dimensional data analysis. Bioinformatics (2018), 34(20), 3479–3487.

4.  Li W, Zhao A, Zhang S*, Li J*. Joint modeling of multiple RNA-seq samples for accurate isoform quantification. The Annals of Applied Statistics (2018), 12(1), 510–539.

5.  Wang C, Zhang S*. Large-scale determination and characterization of cell type-specific regulatory elements in the human genome. Journal of Molecular Cell Biology (2017), 9(6), 463-476. (Editor’s Choice and Cover Story).

6.  Zhang J, Zhang S*. Discovery of cancer common and specific gene sets. Nucleic Acids Research (2017), 45 (10): e86.

7.  Yang X, Gao L, Zhang S*. Comparative pan-cancer DNA methylation analysis reveals cancer common and specific patterns. Briefings in Bioinformatics (2017), 18(5), 761–773.

8.  Chen J, Zhang S*. Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data. Bioinformatics (2016), 32(11):1724-1732.

9.  Yang X, Shao X, Gao L, Zhang S*. Systematic DNA methylation analysis of multiple cell lines reveals common and specific patterns within and across tissues of origin. Human Molecular Genetics (2015), 24(15):4374-4384.

10. Zhang S, Tian D, Tran NH, Choi KP, Zhang L. Profiling the transcription factor regulatory networks of human cell types. Nucleic Acids Research (2014), 42(20):12380-12387.

11. Chen C, Zhang S*, Zhang XS. Discovery of cell-type specific regulatory elements in the human genome by differential chromatin modification analysis. Nucleic Acids Research (2013), 41 (20): 9230-9242.

12. Zhao J, Zhang S*, Wu LY, Zhang XS. Efficient methods for identifying mutated driver pathways in cancer. Bioinformatics (2012), 28(22): 2940-2947.

13. Zhang S, Liu CC, Li W, Shen H, Laird PW, Zhou XJ. Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Nucleic Acids Research (2012), 40(19): 9379-9391.

14. Zhang S, Li Q, Liu J, Zhou XJ. Integrating multiple functional genomic data to define microRNA-gene regulatory modules by a sparse network-regularized multiple matrix factorization method. Bioinformatics (ISMB2011) 2011, 27:i401-i409.

15. Zhang S, Vasishtan D, Xu M, Topf M, Alber F. A fast mathematical programming procedure for simultaneous fitting of assembly components into cryo-EM density maps. Bioinformatics (ISMB2010) 2010, 26(12):i261-i268.