GEN4059 Computational Methods in BioinformaticsBahçeşehir UniversityDegree Programs INDUSTRIAL DESIGNGeneral Information For StudentsDiploma SupplementErasmus Policy StatementNational QualificationsBologna Commission
INDUSTRIAL DESIGN
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 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