Course Details Language of Instruction English Level of Course Unit Master's Degree Department / Program ELECTRICAL AND COMPUTER ENGINEERING Type of Program Formal Education Type of Course Unit Elective Course Delivery Method Face To Face Objectives of the Course O1. Learn different types and sources of data available in bioinformatics, O2. Learn the fundamental computational problems in molecular biology and genomics, O3. Learn a core set of widely used algorithms in bioinformatics, O4. Learn a set of algorithms that have important applications in bioinformatics, but which have key applications outside of biology as well. O5. Apply the concepts learned to a real problem Course Content W1 Description of the basic terms in Molecular Biology, Genetics and bioinformatics: a) The organization of DNA, proteins, cell; b) In silico biology W2 Review of real world applications of bioinformatics, Introduction of Fragment Assembly Problem W3 Description of Fragment Assembly Problem, Overlap-Layout- Consensus Algorithm W4 Description of Pairwise alignment of biomolecular sequences: Global alignment W5 Description of Local alignment, Semi-global alignment W6 Description of similarity search algorithms such as BLAST algorithm; description of the scoring in similarity matrices: PAM and BLOSUM matrices W7 Description of the Multiple sequence alignment: a) Iterative Methods, b) Structure Based Methods LO1, LO3 W8 Description of the scoring in multiple alignments W9 Description and review of the high-throughput biological data analysis methods: Detecting differential gene expression, multiple hypothesis testing, false-discovery-rate methods. W10 Description and review of the clustering and classification algorithms for gene expression data analysis. W11 Description of the protein-protein interaction, protein/DNA interaction, gene/protein interaction networks W12 Construction and analysis of large scale biological networks W13 Identification of Drug-Repurposing candidates using Biological Networks W14 Description and review of the machine learning approaches for integrating data in molecular biology, genetics and medicine. Course Methods and Techniques Prerequisites and co-requisities None Course Coordinator Asist Prof.Dr. BURCU GÜNGÖR burcu.gungor@agu.edu.tr Name of Lecturers None Assistants None Work Placement(s) No Recommended or Required Reading Resources

 





https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77742&lang=en