COMPUTER ENGINEERING
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
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.

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
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.

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 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.

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:	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.

Evaluation System

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

ECTS / Workload Table

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

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)	Adequate knowledge in mathematics, science and computer engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems.
2)	Ability to identify, formulate, and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose.	2
3)	Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose.	3
4)	Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in computer engineering applications; ability to use information technologies effectively.
5)	Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or computer engineering research topics.	3
6)	Ability to work effectively within and multi-disciplinary teams; individual study skills.	2
7)	Ability to communicate effectively in verbal and written Turkish; knowledge of at least one foreign language; ability to write active reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8)	Awareness of the necessity of lifelong learning; ability to access information, to follow developments in science and technology and to renew continuously.
9)	To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in engineering applications.
10)	Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development.
11)	Knowledge of the effects of engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in engineering; awareness of the legal consequences of engineering solutions.