BAHÇEŞEHİR UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| BUSINESS ADMINISTRATION | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
Course Introduction and Application Information
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| GEN4059 | Computational Methods in Bioinformatics | Spring Fall |
3 | 0 | 3 | 6 |
| This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Basic information
| 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. |
Learning Outcomes
|
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. |
Course Content
| 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. |
Weekly Detailed Course Contents
| 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 |
Sources
| 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. |
Evaluation System
| 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 | |
ECTS / Workload Table
| 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 | ||
Contribution of Learning Outcomes to Programme Outcomes
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Being able to identify problems and ask right questions | |
| 2) | Having problem solving skills and developing necessary analytical attitude | |
| 3) | Comprehending theoretical arguments along with counter arguments in detail | |
| 4) | Gaining awareness of lifelong learning and being qualified for pursuing graduate education | |
| 5) | Applying theoretical concepts in project planning | |
| 6) | Communicating efficiently by accepting differences and carrying out compatible teamwork | |
| 7) | Increasing efficiency rate in business environment | |
| 8) | Developing innovative and creative solutions in face of uncertainty | |
| 9) | Researching to gather information for understanding current threats and opportunities in business | |
| 10) | Being aware of the effects of globalization on society and business while deciding | |
| 11) | Possessing digital competence and utilizing necessary technology | |
| 12) | Communicating in at least one foreign language in academic and daily life | |
| 13) | Possessing managing skills and competence | |
| 14) | Deciding with the awareness of the legal and ethical consequences of business operations | |
| 15) | Expressing opinions that are built through critical thinking process in business and academic environment |