• Course Information
  • Course Title Code Semester Laboratory+Practice (Hour) Pool Type ECTS
    Bioinformatics and Biotechnology BİL466 FALL-SPRING 3+0 E 6
    Learning Outcomes
    1- query biological data
    2- interpret and model biological information
    3- understand and apply basic solution methods developed for biological problems
    4- design and implement bioinformatics algorithms / methods
    5-search the latest scientific publications on bioinformatics methods developed for a given problem
  • ActivityPercentage


    NumberTime (Hours)Total Workload (hours)
    Course Duration (Weeks x Course Hours)14342
    Classroom study (Pre-study, practice)14798
    Short-Term Exams (exam + preparation) 0000
    Midterm exams (exam + preparation)3011010
    Laboratory 0000
    Final exam (exam + preparation) 4011212
    Other 0000
    Total Workload (hours)   172
    Total Workload (hours) / 30 (s)     5,73 ---- (6)
    ECTS Credit   6
  • Course Content
  • Week Topics Study Metarials
    1 Evolution, DNA, RNA, Biology and Computation
    2 Biological Relations, PAM and BLOSUM Matrices
    3 BLOSUM Matrices
    4 Sequence Analysis
    5 Dynamic programming
    6 BLAST
    7 Multiple sequence sequencing-I
    8 Midterm exams
    9 Multiple sequence sequencing-II
    10 Learning Algorithms
    11 Probability Models
    12 Auditing learning
    13 Unregulated learning
    14 Graph Algorithms
    15 Latest trends / trends
    Prerequisites -
    Language of Instruction Turkish
    Coordinator Assist. Prof. Dr. Seda ŞAHİN
    Instructors -
    Assistants -
    Resources 1. Zvelebil M., Baum J.: Understanding bioinformatics. Garland Science, London, 2007 ISBN 978-0815340249 2. Neil C. Jones and Pavel A. Pevzner, An Introduction to Bioinformatics Algorithms, The MIT Press, 2004 (ISBN-13: 978-0262101066). 3. Introduction To Algorithms Third Edition, THOMAS H. CORMEN CHARLES E. LEISERSON RONALD L. RIVEST CLIFFORD STEIN, The MIT Press Massachusetts Institute of Technology Cambridge, 2001.
    Supplementary Book -
    Goals to provide the student with competence in solving computational and modeling problems in the field of bio-informatics.
    Content Evolution, DNA, RNA, Biology and Computation, Biological Relations, PAM and BLOSUM Matrices, Sequence Analysis, Dynamic programming, BLAST, Multiple sequence sequencing, Learning Algorithms, Probability Models, Auditing learning, Unregulated learning, Graph Algorithms, Latest trends / trends
  • Program Learning Outcomes
  • Program Learning Outcomes Level of Contribution
    1 To be able to apply mathematics, science and engineering theories and principles to Computer Engineering problems. 3
    2 To have the ability to define, model, and solve problems related to Computer Engineering. 2
    3 To be able to design and conduct experiments, as well as to analyze and interpret data. 1
    4 To be able to design and analyze a process for a specific purpose within technical and economical limitations. 1
    5 To be able to use modern techniques and calculation tools required for engineering applications. 2
    6 To have the awareness of professional liabilities and ethics. 2
    7 To be able to get involved in interdisciplined and multidisciplined team work. 4
    8 To be able to declare his/her opinions orally or written in a clear, concise and brief manner. -
    9 To improve him/herself by following the developments in science, technology, modern issues, and know the importance of lifelong learning. 1
    10 To be able to evaluate engineering solutions for the global and social problems especially for the health, safety, and environmental problems. -
    11 To have knowledge about of contemporary issues. -
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