Course Details

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1BENG629BİYOİNFORMATİKTE ALGORİTMALAR3+0+037,513.05.2025

 
Course Details
Language of Instruction English
Level of Course Unit Doctorate's Degree
Department / Program BIOENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course Providing a strong theoretical foundation in algorithmic principles essential for solving bioinformatics problems.
Applying algorithms to analyze genomic, proteomic, and phylogenetic data.
Evaluating the strengths and limitations of algorithms in bioinformatics.
Developing innovative computational approaches for bioinformatics challenges
Course Content This course explores both fundamental and advanced algorithms essential for computational biology and bioinformatics. It includes topics such as pairwise and multiple sequence alignment, phylogenetic analysis, and structural bioinformatics, with a focus on the mathematical and computational principles behind these methods. Students will gain hands-on experience in implementing algorithms, analyzing their performance, and applying these techniques to real-world biological datasets. Additionally, advanced topics such as graph theory and the application of machine learning in bioinformatics will be introduced. The course aims to equip students with the skills to critically evaluate existing tools and develop innovative computational solutions to address emerging challenges in the field.
Course Methods and Techniques The course will be delivered through instructor-led theoretical lectures. Weekly topics will be supported by current scientific literature, with emphasis on discussing key studies and findings. To enhance student engagement, interactive methods such as Q&A sessions and open discussions will be employed. Students will be expected to conduct individual readings and analyze scientific articles related to specific topics.
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Associate Prof.Dr. Duygu Saçar Demirci duygu.sacar@agu.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources An Introduction to Bioinformatics Algorithms
Course Notes To Be Announced weekly via Canvas

Course Category
Mathematics and Basic Sciences %100

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Sunum/Seminer 2 % 60
Final examination 1 % 40
Total
3
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
F2F Dersi 14 3 42
Sunum için Hazırlık 2 15 30
Sunum 2 2 4
Okuma 10 7 70
Araştırma 10 5 50
Ders dışı çalışma 10 2 20
Final Sınavı 1 10 10
Total Work Load   Number of ECTS Credits 7,5 226

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Discuss key algorithms used in bioinformatics and their biological applications
2 Evaluate algorithms for sequence alignment, phylogenetics, and structure analysis.
3 Analyze the efficiency and accuracy of algorithms in solving biological problems
4 Evaluate algorithmic approaches and their applications using scientific literature
5 Design novel computational solutions to address specific challenges in bioinformatics

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction
2 Fundamentals of Sequence Alignment
3 Heuristic Methods for Sequence Alignment
4 Multiple Sequence Alignment
5 Phylogenetic Tree Construction
6 Hidden Markov Models (HMMs) in Bioinformatics
7 Gene Prediction and Annotation Algorithms
8 Fall Break
9 RNA and Protein Structure Prediction
10 Genetic Algorithms in Bioinformatics
11 Graph Algorithms in Bioinformatics
12 Machine Learning in Bioinformatics
13 Systems Biology and Network Analysis
14 Algorithmic Challenges
15 Presentations
16 Final

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
C1 5 3 2 1 2 4 3 2 2
C2 5 3 3 2 1 2 5 3 3 2 3
C3 5 3 2 2 1 2 4 3 3 1 4
C4 4 3 2 2 1 2 4 4 4 1 5
C5 5 4 3 2 1 2 5 4 3 2 5

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77701&lang=en
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