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| TBB6021 | Biological Databases and Datamining | 2+2+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of BIOSTATISTICS and MEDICAL INFORMATICS | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face, Practical | | Contact Hours | 14 weeks - 2 hours of lectures and 2 hours of practicals per week | | Lecturer | -- | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The aim of the course is to provide the ability to integrate different types of biological data and databases and to analyze biological data. Moreover, the aim of this course is to provide the ability to create original databases on the basis of MySQL or SQLite using different types of biological data and analyze them with different packages in the R programming language. In this course, it will be used machine-learning methods such as Support Vector Machines and Multiple Regressions on experimental data to classify and predict gene function and regulation. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Identify different types of biological data and know biological databases. | | 1,5 | | PO - 2 : | Knowledge of database structure and database design using biological data. | | 1,3,4 | | PO - 3 : | To be able to analyze and evaluate gene function predictions and classifications in biological databases using machine learning methods. | | 1,4,5 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| The course is divided into three sections:
1) Introduction to MySQL and R
2) Introduction to different data types
3) Machine learning methods for data mining
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Biomolecules, bioinformatics, basic terminology | | | Week 2 | Introduction to databases / Basic SQL: MySQL vs SQLite. | | | Week 3 | Complex SQL queries / Using indexes | | | Week 4 | Genome Databases (Browsers, Resources, File Formats) / Functional Annotations: (GO-terms) /Writing Functions in R | | | Week 5 | Transcriptome Databases / Pathway and Gene Regulatory databases | | | Week 6 | Protein Interaction Databases (Biogrid, String) / Building and Querying an Interaction Network Database | | | Week 7 | Creating a database for integrating different Biological data types | | | Week 8 | Mid-term Exam | | | Week 9 | Differential Gene Expression / Correlation / Clustering | | | Week 10 | Decision Trees / Installing RWeka | | | Week 11 | Logistic Regression | | | Week 12 | Evaluating predictions (GO-term enrichment and ROC curves) | | | Week 13 | Article discussion | | | Week 14 | General review | | | Week 15 | Student presentations | | | Week 16 | Final Exam | | | |
| 1 | Zvelebil M., Baum J. O. 2007 Understanding bioinformatics, Garland Science. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 8 | | 1 | 30 | | Presentation | 14 | | 1 | 20 | | End-of-term exam | 15 | | 1 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 4 | 14 | 56 | | Sınıf dışı çalışma | 8 | 14 | 112 | | Arasınav için hazırlık | 2 | 7 | 14 | | Arasınav | 2 | 1 | 2 | | Ödev | 10 | 10 | 100 | | Proje | 2 | 6 | 12 | | Dönem sonu sınavı için hazırlık | 2 | 16 | 32 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 330 |
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