ISTINYE UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| Molecular Oncology (DR) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
Course Introduction and Application Information
| Course Code: | MONK6112 | ||||
| Course Name: | Bioinformatics and Artificial Intelligence in Cancer 1 | ||||
| Semester: | Spring | ||||
| Course Credits: |
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| Language of instruction: | Turkish | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||
| Course Coordinator: | Dr. Öğr. Üy. ASLI KUTLU | ||||
| Course Lecturer(s): | ASLI KUTLU & LEVENT KORKMAZ | ||||
| Course Assistants: |
Course Objective and Content
| Course Objectives: | The objective of this course for each student is summarized as using web-based bioinformatic tools in effective manner, performing mathematical modelling-based analysis, and interpreting the obtained results according to developed mathematical models. In the second part of this course, it is aimed to teach basics of decision making algorithms and artificial intelligence approaches to students. |
| Course Content: | The content of this course is the effective use of bioinformatics and artifical intelligence tools to understand more about cancer research and molecular applications in the field of oncology. Another important content of the course is to convey basic information about the effective use of existing mathematical models during the experimental design phase and the interpretation of the results. To achieve this goal, the basis of many bioinformatics tools together with artificial intelligence tools is considered as main content of this course. |
Learning Outcomes
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The students who have succeeded in this course;
1) Knows the concept of 'big data' in cancer, and uses this concept effectively 2) Knows the scope of mathematical models and assumptions 3) Can effectively use the bioinformatics tools used extensively in cancer. 4) Masters the algorithm behind online bioinformatics tools, and can target algorithms and parameters used when needed 5) Effective variant analysis after next generation sequencing 6) Dominates gene, protein and miRNA interaction networks 7) Has knowledge of models and tools used in cancer systems biology 8) Knows the whole of the methods of targeted drug design 9) Masters the fundamental of artificial intelligence 10) Has an idea about the effective use of decision making algoritms |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Introduction to Bioinformatics & Basic molecular biology techniques | PPT |
| 2) | Sequencing algorithms | ppt |
| 3) | Multiple Sequencing approach | PPT |
| 4) | Multiple sequencing apprpach 2 | ppt |
| 5) | Database Research: PAM & Blossum, and FASTA 1 | |
| 6) | Database Research: PAM & Blossum, and FASTA 2 | PPT |
| 7) | Midterm | ppt |
| 8) | The combination of genomics and transcriptomics data for somatic variant analysis 1 | ppt |
| 9) | The combination of genomics and transcriptomics data for somatic variant analysis 2 | ppt |
| 10) | Data mining and decision support systems | PPT |
| 11) | Clinical data profiling in cancer | PPT |
| 12) | Clinical data profiling in cancer 2 | ppt |
| 13) | Building cancer artificial intelligence nodes | PPT |
| 14) | General review | PPT |
Sources
| Course Notes / Textbooks: | Cancer Bioinformatics by Ying X, Juan Cui and David Puett (Springer) Artificial intelligence in cancer- Diagnostic to Tailored treatment by Smaranda Belciug |
| References: | Cancer Bioinformatics by Ying X, Juan Cui and David Puett (Springer) Artificial intelligence in cancer- Diagnostic to Tailored treatment by Smaranda Belciug |
Course - Program Learning Outcome Relationship
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
|---|---|---|---|---|---|---|---|---|---|---|
| Program Outcomes | ||||||||||
| 1) To be able to define, evaluate and use current and advanced knowledge of cancer and molecular cancer | 3 | 3 | 2 | 2 | 2 | 2 | 3 | 3 | 3 | 3 |
| 2) To be able to research, comprehend and analyze a scientific and technological issue in the field of molecular cancer with a systematic approach. | 3 | 1 | 1 | 1 | 2 | 2 | 2 | 3 | 3 | 3 |
| 3) Ability to develop, design, adapt and implement new and complex ideas about the molecular mechanisms of cancer at an expert level with original thinking and/or research | 2 | 3 | 2 | 2 | 3 | 2 | 3 | 3 | 3 | 3 |
| 4) To be able to comprehend the interdisciplinary interaction with which the molecular cancer field is related, to develop a new method, design and/or application with a new idea or a known idea. | 2 | 3 | 2 | 2 | 3 | 2 | 3 | 3 | 3 | 3 |
| 5) To gain the ability to synthesize, analyze, interpret, question and criticize information about treatment approaches developed against cancer | 3 | 3 | 2 | 1 | 2 | 2 | 2 | 2 | 3 | 2 |
| 6) Tp develop new ideas and methods in the field by using high-level skills such as problem solving and decision making in their studies. | 3 | 3 | 3 | 3 | 1 | 1 | 1 | 3 | 3 | 3 |
| 7) To have good communication skills in their work and being able to critically examine and develop the norms that guide these relationships, and to manage actions to change them when necessary | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 8) To have an awareness of ethical responsibility in their research and to be able to defend their findings by using advanced written, verbal and visual communication. | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 3 |
| 9) To contribute to the solution of social, scientific, computational and ethical problems encountered in the field of molecular cancer and to support the development of these values. | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 10) To be able to interpret research and study results and expand the limits of knowledge in the field by producing an original work. | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
Course - Learning Outcome Relationship
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | To be able to define, evaluate and use current and advanced knowledge of cancer and molecular cancer | 3 |
| 2) | To be able to research, comprehend and analyze a scientific and technological issue in the field of molecular cancer with a systematic approach. | 2 |
| 3) | Ability to develop, design, adapt and implement new and complex ideas about the molecular mechanisms of cancer at an expert level with original thinking and/or research | 2 |
| 4) | To be able to comprehend the interdisciplinary interaction with which the molecular cancer field is related, to develop a new method, design and/or application with a new idea or a known idea. | 2 |
| 5) | To gain the ability to synthesize, analyze, interpret, question and criticize information about treatment approaches developed against cancer | 3 |
| 6) | Tp develop new ideas and methods in the field by using high-level skills such as problem solving and decision making in their studies. | 3 |
| 7) | To have good communication skills in their work and being able to critically examine and develop the norms that guide these relationships, and to manage actions to change them when necessary | 1 |
| 8) | To have an awareness of ethical responsibility in their research and to be able to defend their findings by using advanced written, verbal and visual communication. | 2 |
| 9) | To contribute to the solution of social, scientific, computational and ethical problems encountered in the field of molecular cancer and to support the development of these values. | 2 |
| 10) | To be able to interpret research and study results and expand the limits of knowledge in the field by producing an original work. | 3 |
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Midterms | 1 | % 40 |
| Final | 1 | % 60 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| total | % 100 | |
Workload and ECTS Credit Calculation
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 42 |
| Study Hours Out of Class | 14 | 56 |
| Midterms | 1 | 10 |
| Final | 1 | 15 |
| Total Workload | 123 | |