SOFTWARE 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 | Fall | 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) | Be able to specify functional and non-functional attributes of software projects, processes and products. | |
2) | Be able to design software architecture, components, interfaces and subcomponents of a system for complex engineering problems. | |
3) | Be able to develop a complex software system with in terms of code development, verification, testing and debugging. | |
4) | Be able to verify software by testing its program behavior through expected results for a complex engineering problem. | |
5) | Be able to maintain a complex software system due to working environment changes, new user demands and software errors that occur during operation. | |
6) | Be able to monitor and control changes in the complex software system, to integrate the software with other systems, and to plan and manage new releases systematically. | |
7) | Be able to identify, evaluate, measure, manage and apply complex software system life cycle processes in software development by working within and interdisciplinary teams. | |
8) | Be able to use various tools and methods to collect software requirements, design, develop, test and maintain software under realistic constraints and conditions in complex engineering problems. | |
9) | Be able to define basic quality metrics, apply software life cycle processes, measure software quality, identify quality model characteristics, apply standards and be able to use them to analyze, design, develop, verify and test complex software system. | |
10) | Be able to gain technical information about other disciplines such as sustainable development that have common boundaries with software engineering such as mathematics, science, computer engineering, industrial engineering, systems engineering, economics, management and be able to create innovative ideas in entrepreneurship activities. | |
11) | Be able to grasp software engineering culture and concept of ethics and have the basic information of applying them in the software engineering and learn and successfully apply necessary technical skills through professional life. | |
12) | Be able to write active reports using foreign languages and Turkish, understand written reports, prepare design and production reports, make effective presentations, give clear and understandable instructions. | |
13) | Be able to have knowledge about the effects of engineering applications on health, environment and security in universal and societal dimensions and the problems of engineering in the era and the legal consequences of engineering solutions. |