《Bioinformatics Computing》Course Syllabus
Author:管理员 Time:2017-04-11 Hit:250

《Bioinformatics Computing》Course Syllabus

Course Name

Bioinformatics Computing

Instructor

Prof. Xiujuan Lei

Course Type

Elective Course

Prerequisite Courses

Bioinformatics, Data Mining, Pattern Recognition, Intelligent Computing and Intelligent   Optimization Methods

Discipline

Computer Science and Technology

Learning Method

Mentoring, discussion and   programing

Semester

2nd semester

Hours

40

Credit

2

 

1. Objective & Requirement

This course combines basic questions in bioinformatics with pattern recognition and artificial intelligence methods and the latest research in recent years to teach postgraduate students whose major is computer science and technology. This course is intended to introduce bioinformatics and related calculation, identification and optimization theoretically and technically, helps students to lay the foundations of their research in bioinformatics computing related fields. The main content of this course include: basic knowledge of bioinformatics, clustering methods in pattern recognition and applications of swarm intelligence optimization algorithm in multiple sequence alignment, gene data and protein-protein interaction networks. The students should make study notes coving the main content of the course in their studying, accomplish an application which combing their major and bioinformatics computing, then completing the task of learning this course. Prerequisite Courses including Bioinformatics, data mining, pattern recognition, Intelligent Computing and Intelligent Optimization Methods.

2. Primary coverage

This course is based on biological information, combines data mining, pattern recognition and intelligent computing methods to expounds the following contents: basic concepts of biology, bioinformatics history introduction and overview, acquisition, storage and query of sequence; clustering methods (including partition-based method, density-based method, hierarchy-based method and graph mining), sequence alignment (basic concepts, scoring matrix and algorithms) and the applications of swarm intelligence optimization algorithm in multiple sequence alignment, gene data and protein-protein interaction networks.

3. Textbook

  1. Pierre Baldi, Soren Brunak, Bioinformatics: The Machine Learning Approach, Bradford Books; 2nd Revised edition, 2001

  2. D. W. MountBioinformatics—Sequence and Genome Analysis, Cold Spring Harbor Laboratory Press,U.S.; 2nd Revised edition, 2002

  3. Jiawei Han, Micheline Kamber. Data Mining: Concepts and Techniques. Second Edition, Elsevier Inc2006

  4. A. Zhang, Protein Interaction Networks: Computational Analysis, Cambridge University Press, 2009

  5. L. Chen, R-Q. Wang, C. Li, K. Aihara, Modelling Biomolecular Networks in Cells: Structures and Dynamics, Springer-Verlag, London, 2010

    4. Reference

  6. L. Chen, R. Liu, Z. Liu, M. Li, K. Aihara. Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Scientific Reports, 2012

  7. Y.R. Cho, W. Hwang, A.D. Zhang. Optimizing Flow-based Modularization by Iterative Centroid Search in Protein Interaction Networks. Proceedings of 7th IEEE International Conference on Bioinformatics and Bioengineer, Oct. 14-17, 2007, pp.342-349

  8. F. Wu, J. Huan. Special Focus on Bioinformatics and Systems Biology, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2011, 8(2): 292-293

  9. J. Wang, M. Li, H. Wang, Y. Pan. Identification of Essential Proteins Based on Edge Clustering Coefficient. IEEE/ACM Trans. Comput. Biology Bioinform. 2012, 9(4): 1070-1080

  10. G. Sun, L. Gao, S.S. Han. Identification of overlapping and non-overlapping community structure by fuzzy clustering in complex networks. Information Sciences, 2011, 181(6): 1060-1071

  11. S. Zhang, Y. Li, L. Xia, Q. Pan. PPLook: an automated data mining tool for protein-protein interaction. BMC Bioinformatics, 2010, 11: 326

  12. X. Lei, S. WuL. Ge, A. Zhang. Clustering and Overlapping Modules Detection in PPI Network Based on IBFO. Proteomics, Jan.2013, 13(2): 278-290

  13. X. Lei, J. Tian, L. Ge, A. Zhang. The Clustering Model and Algorithm of PPI Network Based on Propagating Mechanism of Artificial Bee Colony. Information Sciences (May.2013DOIhttp://dx.doi.org/10.1016/j.ins.2013.05.027 )

    5. Course Evaluation (Tentative)

    Paper Explanation                   50%

    Class Discussion              10%

    Essay                                        40%