Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
ARTIFICIAL INTELLIGENCE IN GENOMICS 803400815131 3 + 0 3.0 10.0

Prerequisites None

Language of Instruction English
Course Level Graduate Degree
Course Type Elective
Mode of delivery
Course Coordinator
Instructors
Assistants
Goals To give theoretical and practical knowledge about the applications of genomics based on artificial intelligence and machine learning and make the students competent to make research and analysis on the topic.
Course Content Genome Sequencing, Gene Regulation, Pharmacogenomics, Genetic Screening Tools, Agrigenomics, Functional Genomics, Structural Genomics, Evolutionary Algorithms, Biological Algorithms, DNA-RNA and Proteins, Transcription Factor, Convolutional Models, Chromatin Accessibility, RNA Interference
Learning Outcomes 1) Acquires knowledge about modern techniques used in AI applications used in genomics
2) Review and evaluate national and international literature about AI applications used in genomics
3) Prepares a research report about a selected topic in AI applications used in genomics.

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week DNA-RNA and Proteins Lecture

 Homework
2. Week Evolutionary Algorithms Lecture

 Homework
3. Week Biological Algorithms Lecture

 Homework
4. Week RNA Interference Lecture

 Homework
5. Week Transcription Factor Lecture

 Homework
6. Week Chromatin Accessibility Lecture

 Homework
7. Week Convolutional Models Lecture

 Homework
8. Week Genome Sequencing Lecture

 Homework
9. Week Genetic Screening Tools Lecture

 Homework
10. Week Gene Regulation Lecture

 Homework
11. Week Functional Genomics Lecture

 Homework
12. Week Structural Genomics Lecture

 Homework
13. Week Farmakogenomik Lecture

 Homework
14. Week Agrigenomik Lecture

 Homework

Sources Used in This Course
Recommended Sources
Deep learning for genomics. (2018). Nature Genetics, 51(1), 1–1. doi:10.1038/s41588–018–0328–0 Dias, Raquel, and Ali Torkamani. "Artificial intelligence in clinical and genomic diagnostics." Genome medicine 11.1 (2019): 1-12.
Ramsundar, Bharath, et al. Deep learning for the life sciences: applying deep learning to genomics, microscopy, drug discovery, and more. " O'Reilly Media, Inc.", 2019. Ramsundar, Bharath, et al. Deep learning for the life sciences: applying deep learning to genomics, microscopy, drug discovery, and more. " O'Reilly Media,
Siddique, Nazmul. Intelligent control: a hybrid approach based on fuzzy logic, neural networks and genetic algorithms. Vol. 517. Springer, 2013.

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3
PY15555
PY25555
PY35555

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 3
Work Hour outside Classroom (Preparation, strengthening) 14 9
Homework 2 20
Presentation (Including Preparation Time) 1 10
Project (Including Preparation and presentation Time) 1 1
Report (Including Preparation and presentation Time) 1 1
Midterm Exam 1 1
Time to prepare for Midterm Exam 1 20
Final Exam 1 1
Time to prepare for Final Exam 1 50
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
Quick Access Hızlı Erişim Genişlet
Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload