Dr. Changhui Yan


Phone:  701-231-8182

Office: QBB258 A27

Email: Changhui.Yan@ndsu.edu

Office Hours: 1:00pm-2:00pm MWF


Ph.D., Iowa State University (2005). Majors: Computer Science; Bioinformatics & Computational Biology.

Research Interests:

      Bioinformatics, Computational Biology

      Machine Learning, Data Mining

      Cloud Computing

Research Projects:

1.     ABI Innovation: Computational Methods for Macromolecular Binding Analysis

In this project Dr. Yan develops computational methods for automated discovery of structural and physical-chemical elements contributing to the affinity and specificity of macromolecular binding. To achieve this goal, Dr. Yan develops graph models for the representation of protein structures and graph kernel-based machine-learning methods for the analysis and prediction of binding sites. The proposed graph models provide a succinct data structure to encode a range of structural and physical properties germane to molecular interactions. Dr. Yan uses an innovative graph-kernel-based approach to investigate the modular organization of binding sites and discover characteristic patterns associated with the modules.


                             i.        Sun, Q. and Yan, C. Mining Structure Patterns on the Protein-DNA Interfaces. Under Review.

                           ii.        Cheng, W. and Yan, C. A Graph Approach to Mining Biological Patterns in the Binding Interfaces. Journal of Computational Biology. (Accepted. To appear 2016).

                         iii.        Cheng, W., and Yan, C. Mining Graph Patterns in the Protein-RNA Interfaces. In Proceedings the IEEE International Conference on Bioinformatics and Biomedicine, 2015, 1267-1271.

                          iv.        Yan, C., Y. Wang. A Graph Kernel Method for DNA-Binding Site Prediction. BMC Systems Biology 2014, 8(Suppl 4): S10.

                            v.        Zhu, Y., and Yan, C. Graph methods for predicting the function of chemical compounds. In Proceedings of the 2014 IEEE International Conference on Granular Computing. 2014, 386 390.

                          vi.        Wen, C., and Yan, C. A method for discriminating native protein-DNA complexes from decoys using spatial specific scoring matrices. In Proceedings of the 7th International Conference on Systems Biology, 2013, 115-119.

                        vii.        Sanjak, M., and Yan, C. Prediction of enzyme catalytic sites on protein using a graph kernel method. In Proceedings of the 7th International Conference on Systems Biology, 2013, 32-34.

                      viii.        Alvarez, M. and Yan, C. A new protein graph model for function prediction. J Comp. Bio. Chem. 2012, 376-10.

                          ix.        Alvarez, M. and Yan, C. A graph-based semantic similarity measure for the gene ontology. J Bioinform Comput Biol. 2011, 9(6): 115.

                            x.        Alvarez, M., Qi, X. and Yan, C. A shortest-path graph kernel for estimating gene product semantic similarity. J Biomed Semantics, 2011, 2:3.


2.     II-New: ABC-A Biology Cloud

The ongoing revolution in next generation sequencing (NGS) technologies and large-scale structural genomics projects has led to dramatic increase in genomic sequences and protein structures. This has brought biological research into a data-driven era, where computational methods and facilities are needed for handling and analyzing the huge volumes of data. In this project, the PIs develop a cloud computing infrastructure called A Biology Cloud (ABC) to support research in the areas of bioinformatics and computational biology. ABC is built based on the OpenStack, which is supported by more than 180 large companies and has quickly become the standard for cloud infrastructure. ABC will enable researchers at North Dakota State University (NDSU) to conduct pioneering research in their respective fields and promote and facilitate cross-disciplinary collaborations among them.


                           i.          Xu Y, Yang X, Zhao P, Yang Z, Yan C, Guo B, Qian SY. Knockdown of delta-5-desaturase promotes the anti-cancer activity of dihomo-γ-linolenic acid and enhances the efficacy of chemotherapy in colon cancer cells expressing COX-2. Free Radic Biol Med. 2016 Jul;96:67-77.

                         ii.          Horvath DP, Hansen SA, Moriles-Miller JP, Pierik R, Yan C, Clay DE, Scheffler B, Clay SA. RNAseq reveals weed-induced PIF3-like as a candidate target to manipulate weed stress response in soybean. New Phytol. 2015 Jul;207(1):196-210

                       iii.          Singh RK, Cho K, Padi SK, Yu J, Haldar M, Mandal T, Yan C, Cook G, Guo B, Mallik S, Srivastava DK. Mechanism of N-Acylthiourea-mediated activation of human histone deacetylase 8 (HDAC8) at molecular and cellular levels. J Biol Chem. 2015 Mar 6;290(10):6607-19.

                       iv.          Leboldus JM, Kinzer K, Richards J, Ya Z, Yan C, Friesen TL, Brueggeman R. Genotype-by-sequencing of the plant-pathogenic fungi Pyrenophora teres and Sphaerulina musiva utilizing Ion Torrent sequence technology. Mol Plant Pathol. 2015 Aug;16(6):623-32.

                         v.          Shjerve RA, Faris JD, Brueggeman RS, Yan C, Zhu Y, Koladia V, Friesen TL. Evaluation of a Pyrenophora teres f. teres mapping population reveals multiple independent interactions with a region of barley chromosome 6H. Fungal Genet Biol. 2014 Sep;70:104-12.