NURSING (ENGLISH)
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	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
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)	"To plan and assess nursing care within a holistic approach, in accordance with theoretical and evidence-based practices. "
2)	To act in accordance with ethical principles and values in Nursing practices.
3)	To use life-long learning, problem-solving and critical thinking skills.
4)	To use Nursing models/theories in health promotion, protection and care.
5)	To take part in research, projects and activities within sense of social responsibility and interdisciplinary approach.
6)	"To have skills for training and consulting according to health education needs of individual, family and the community. "
7)	"To be sensitive to health problems of the community and and to able to offer solutions. "
8)	"To be able to use interpersonal and intercultural communication skills effectively in Nursing pratices. "
9)	"To be able to use healthcare/information technologies in Nursing practice and research. "
10)	To be able to search for literature in health sciences databases and information sources to access to information and use the information effectively.
11)	To be able to monitor occupational information using at least one foreign language, to collaborate and communicate with colleagues at international level.
12)	To take responsibility and lead in events in order to contribute to health services and Nursing profession.