ARTIFICIAL INTELLIGENCE ENGINEERING | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
MBG4059 | 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 |
Course Objectives: | The goal of this course is to provide an understanding of the fundamental computational methods used in bioinformatics and the set of algorithms that have important applications both inside and outside of the bioinformatics field. |
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 thorough 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: | Haftalık ders notları iletilecektir. Weekly course notes will be provided. |
References: | 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 | % 15 |
Midterms | 1 | % 25 |
Final | 1 | % 50 |
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 | 6 | 84 |
Presentations / Seminar | 5 | 4 | 20 |
Midterms | 1 | 2 | 2 |
Final | 1 | 2 | 2 |
Total Workload | 150 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Have sufficient background in mathematics, science and artificial intelligence engineering. | |
2) | Use theoretical and applied knowledge in the fields of mathematics, science and artificial intelligence engineering together for engineering solutions. | |
3) | Identify, define, formulate and solve engineering problems, select and apply appropriate analytical methods and modeling techniques for this purpose. | |
4) | Analyse a system, system component or process and design it under realistic constraints to meet desired requirements; apply modern design methods in this direction. | |
5) | Select and use modern techniques and tools necessary for engineering applications. | |
6) | Design and conduct experiments, collect data, and analyse and interpret results. | |
7) | Work effectively both as an individual and as a multi-disciplinary team member. | |
8) | Access information via conducting literature research, using databases and other resources | |
9) | Follow the developments in science and technology and constantly update themself with an awareness of the necessity of lifelong learning. | |
10) | Use information and communication technologies together with computer software with at least the European Computer License Advanced Level required by their field. | |
11) | Communicate effectively, both verbal and written; know a foreign language at least at the European Language Portfolio B1 General Level. | |
12) | Have an awareness of the universal and social impacts of engineering solutions and applications; know about entrepreneurship and innovation; and have an awareness of the problems of the age. | |
13) | Have a sense of professional and ethical responsibility. | |
14) | Have an awareness of project management, workplace practices, employee health, environment and work safety; know the legal consequences of engineering practices. |