| GASTRONOMY (TURKISH) | |||||
| 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) | - Possess advanced level theoretical and practical knowledge supported by textbooks with updated information, practice equipments and other resources. | |
| 2) | Use of advanced theoretical and practical knowledge within the field. -Interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the field. | |
| 3) | Inform people and institutions, transfer ideas and solution proposals to problems in written and orally on issues in the field. - Share the ideas and solution proposals to problems on issues in the field with professionals and non-professionals by the support of qualitative and quantitative data. -Organize and implement project and activities for social environment with a sense of social responsibility. -Monitor the developments in the field and communicate with peers by using a foreign language at least at a level of European Language Portfolio B1 General Level. -Use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge. | |
| 4) | Evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach. -Determine learning needs and direct the learning. -Develop positive attitude towards lifelong learning. | |
| 5) | Act in accordance with social, scientific, cultural and ethic values on the stages of gathering, implementation and release of the results of data related to the field. - Possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also, environmental protection, worker's health and security. | |
| 6) | Conduct studies at an advanced level in the field independently. - Take responsibility both as a team member and individually in order to solve unexpected complex problems faced within the implementations in the field. - Planning and managing activities towards the development of subordinates in the framework of a project |