Course Details

Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits	Last Updated Date
1	ECE648	BİYOENFORMATİKTE YAPAY ZEKA: ALGORİTMALARDAN UYGALAMALARA	3+0+0	3	7,5	13.05.2025

Course Details

Language of Instruction	English
Level of Course Unit	Doctorate'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	• Developing a comprehensive understanding of the fundamental computational problems, available data types in field of molecular biology, multi-omics and bioinformatics. • Analyzing a core set of widely used algorithms (e.g. dynamic programming algorithms, large language models) in bioinformatics. • Learning a set of machine learning and deep learning algorithms that have important applications in bioinformatics, but which have key applications outside of biology as well. • Modeling real-world bioinformatics problems involving data integration and graph theory challenges.
Course Content	This course introduces computational techniques for analyzing the vast amounts of data generated in the field of digital biology and -omics, produced by high-throughput technologies. The essential algorithms for sequence analysis, as well as for integrative genomic, transcriptomic, epigenomic, and metagenomic analyses, will be presented. Topics to be covered include applications of machine learning, deep learning, and large language models in bioinformatics, as well as graph algorithms for analyzing large-scale biological networks. The course will also showcase real-world applications of these techniques in disease diagnosis, prognosis, treatment, and drug repurposing, highlighting their transformative impact on modern healthcare and precision medicine.
Course Methods and Techniques
Prerequisites and co-requisities	None
Course Coordinator	None
Name of Lecturers	Associate Prof.Dr. Burcu Bakır-Gungor
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources

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
Ödev	2	% 20
Final examination	1	% 40
Total	3	% 60

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Yazılı Sınav	1	45	45
Ev Ödevi	2	40	80
Proje	1	50	50
Yüz Yüze Ders	1	3	3
Final Sınavı	1	50	50
Total Work Load			Number of ECTS Credits 7,5 228

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	Explain the fundamental computational problems and available data sets in the field of molecular biology, genetics and bioinformatics.
2	Formulate real-world bioinformatics problems involving data integration and graph theory challenges
3	Analyze a core set of widely used algorithms and technologies in bioinformatics
4	Critique state-of-the-art machine learning and deep learning algorithms used in the field of bioinformatics

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Dynamic programming, Needleman-Wunsch algorithm for pairwise sequence alignment
2	Smith-Waterman algorithm for pairwise sequence alignment
3	Graph algorithms: - de Bruijn graphs, Overlap Graphs for Sequence Assembly for Genome and Transcriptome Assembly
4	Community Detection Algorithms, Graph Centrality metrics to identify protein complexes, functional modules, hub proteins in Protein-Protein Interaction (PPI) Networks
5	- Shortest Path, Network Propagation Algorithms to find disease-associated genes close to known genes in a PPI network.
6	Bipartite Graphs for metagenomic data analysis
7	Graph Neural Networks to predict new drug-target interactions for drug repurposing studies
8	Machine Learning Algorithms used in Bioinformatics
9	Deep Learning Algorithms used in Bioinformatics
10	Multi-Omics Data Integration Algorithms for Genomics, Transcriptomics, Proteomics, and Metabolomics data analysis
11	Large language models for modelling DNA and proteins
12	Large language models for metagenomics, for identifying diet-microbiome-disease relationships
13	Artificial Intelligence Algorithms for Antimicrobial, Anticancer, Antiinflammatory Peptide Prediction
14	Blockchain technologies for genomics, electronic health records (EHRs)

Contribution of Learning Outcomes to Programme Outcomes

	P1	P2	P3	P4	P5	P6	P7	P8	P9
C1			4		5	3		5
C2	4	5				4	5		4
C3	5	3			4	3
C4	4			5	4	3

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

https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77914&lang=en

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