TEXTILE AND FASHION DESIGN | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
GEN4059 | Computational Methods in Bioinformatics | Spring | 3 | 0 | 3 | 6 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
Type of course: | Non-Departmental Elective |
Course Level: | Bachelor’s Degree (First Cycle) |
Mode of Delivery: | Face to face |
Course Coordinator : | Dr. Öğr. Üyesi ELIZABETH HEMOND |
Recommended Optional Program Components: | There is none. |
Course Objectives: | The goal of this course is to provide an understanding of the fundamental computational methods used in bioinformatics and set of algorithms that have important applications in bioinformatics and also have several other applications outside of bioinformatics. |
The students who have succeeded in this course; 1. Recognize the fundamental models of computation useful in modeling nucleic acid and protein sequences. 2. Design and implement algorithms useful for analyzing various molecular biology data. 3. Discuss Genetic Algorithm and its applications in bioinformatics. 4. Discuss Greedy Algorithms and its applications in bioinformatics. 5. Discuss Gibbs sampling and its applications in bioinformatics. 6. Recognize Expectation Maximization and its applications in bioinformatics. 7. Recognize Hidden Markov models and its applications in bioinformatics. 8. Define Bayesian networks and its applications in bioinformatics. 9. Define graphs and its applications in bioinformatics. |
This course will provide a broad and through background in computational methods and algorithms that are widely used in bioinformatics applications. Various existing methods will be critically described and the strengths and limitations of each will be discussed. |
Week | Subject | Related Preparation |
1) | A brief introduction to computational complexity and algorithm design techniques | |
2) | Exact sequence search algorithms | |
3) | Rabin-Karp algorithm, pattern matching, suffix trees | |
4) | Elements of dynamic programming, Manhattan tourist problem, k-band algorithm | |
5) | Approximate string matching, divide and conquer algorithms | |
6) | Branch and bound search | |
7) | Genetic Algorithm | |
8) | Greedy Algorithms | |
9) | Gibbs sampling | |
10) | Expectation Maximization | |
11) | Hidden Markov models | |
12) | Bayesian networks | |
13) | Graphs | |
14) | Review |
Course Notes / Textbooks: | Relevant course notes or hand-outs will be supplied. |
References: | 1)An Introduction to Bioinformatics Algorithms (Computational Molecular Biology), Neil Jones and Pavel Pevzner, MIT Press, 2004. |
Semester Requirements | Number of Activities | Level of Contribution |
Homework Assignments | 2 | % 10 |
Project | 1 | % 25 |
Midterms | 1 | % 25 |
Final | 1 | % 40 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 35 | |
PERCENTAGE OF FINAL WORK | % 65 | |
Total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Study Hours Out of Class | 14 | 7 | 98 |
Midterms | 1 | 2 | 2 |
Final | 1 | 2 | 2 |
Total Workload | 144 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution |