• Course Description
  
  • This course covers recent developments and open research problems in the area of bioinformatics. Main topics include:
    • Comparative genome analysis,
    • Next generation sequencing,
    • Protein folding problem, prediction of secondary/tertiary structure,
    • Multiple structural alignment, protein docking,
    • Functional classification of proteins, human genome annotation,
    • Statistical modeling of biological data, kernel based methods, hidden Markov models,
    • Data integration,
    • Prediction and mining of genetic networks and protein interaction networks.
  
  
• Lecture Time/Place
  
  • Mondays - 10:40 to 13:30 in G-102.

    Directions to G-102: Computer Center, First floor, at the end of the long bridge corridor.

  
  
• Course Objectives
  
  • The primary objectives of this course are to expose students to recent developments in the field of bioinformatics and to enable students initiate research in this area. Upon completion of this course the students will:
    • be aware of the current challenges in Bioinformatics,
    • have learnt the state-of-the-art methods to tackle important biological problems,
    • and be able to initiate and conduct research in the area of Bioinformatics.
  
  
• Prerequisites
  
  • No formal prerequisites. However, some familiarity with Bioinformatics will help the students get the most benefit out of the course. Students who are new to Bioinformatics are encouraged to take CENG 465 - Introduction to Bioinformatics which provides an overview of the field of Bioinformatics by covering a breadth of well-known algorithms in this area. The topics covered in CENG 465 provide the background material for the more advanced, research oriented topics included in this course (CENG 734).


• Reading Material
  
  • Textbook:
    No textbook is required.
  • Reference Material:
    • A. Tramontano, The Ten Most Wanted Solutions in Protein Bioinformatics, Chapman & Hall/CRC, 2005.
    • S. Karlin, Frontiers of Bioinformatics: Unsolved Problems and Challenges, National Academy Press, 2005.
    • W. J. Ewens and G. Grant, Statistical Methods in Bioinformatics: An Introduction, Springer, 2006.
    • B. Scholkopf, K. Tsuda, and J.-P. Vert, Kernel Methods in Computational Biology, MIT Press, 2004.
    • Additional readings in terms of recent conference and journal articles will be provided throughout the semester.


• Grading Policy
  
  • Reading assignments (short quizzes about reading material) : 40%
  • Class project (teams of 1-4 students) : 40%
  • Final exam : 20%

Tolga Can   (tcan@ceng.metu.edu.tr)
Office Location: Computer Engineering, B-109
Office Hours: By appointment. Contact the instructor