| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Bioinformatics | YZM412 | Elective | Bachelor's degree | 4 | Spring | 5 |
Name of Lecturer(s)
Assistant Prof. Dr. Kaplan KAPLAN
Learning Outcomes of the Course Unit
1) Students learn the usage areas of bioinformatics.
2) Students understand the collection and processing of information.
3) Students can use biological databases.
4) Students can evaluate the results of the sequence analysis.
5) Students become able to make primary design.
Program Competencies-Learning Outcomes Relation
| Program Competencies | |||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | ||
| Learning Outcomes | |||||||||||||
| 1 | High | No relation | No relation | No relation | No relation | Low | No relation | No relation | No relation | No relation | Low | No relation | |
| 2 | Low | No relation | Middle | No relation | No relation | No relation | No relation | High | No relation | No relation | No relation | No relation | |
| 3 | Low | Middle | Low | No relation | No relation | No relation | No relation | High | No relation | No relation | No relation | No relation | |
| 4 | Middle | No relation | No relation | Middle | No relation | No relation | No relation | High | No relation | No relation | No relation | No relation | |
| 5 | High | No relation | No relation | High | No relation | No relation | No relation | High | No relation | No relation | No relation | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Not Required
Course Contents
Bioinformatics and Mathematics Genetic Codes, Matrix and Symmetry Techniques Biological sequences, sequence arrangement, and statistics DNA and Knot theory, Protein structure, Geometry and Topology Biological networks and Graph Theory, Biological Systems, Fractals, ans System Biology Matrix Genetics, Hamarad Matrices and Biology The central dogma of Algebraic Evolutionary Trends and Infromatics Affective informatics Computational approaches to bioinformatics Building biological databases Visualization and data mining Bioinformatics tools.
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
1) Lecture
2) Lecture
3) Question-Answer
4) Question-Answer
5) Discussion
6) Discussion
Assessment Methods and Criteria
Contribution of Midterm Examination to Course Grade |
40% |
|---|---|
Contribution of Final Examination to Course Grade |
60% |
Total |
100% |
Language of Instruction
Turkish
Work Placement(s)
Not Required