| Language of instruction : English |
| | | Exam contract: not possible |
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Sequentiality
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No sequentiality
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Change the language, year or choose another item in the dropdown list if it is available.
| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 2nd year Master Bioinformatics - icp | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical |  |
| 2nd year Master Biostatistics - icp | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 2nd year Master Quantitative Epidemiology - icp | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software. | | | - DC
| ... correctly using state-of-the-art analysis methodology. | | | - DC
| ... correctly using state-of-the-art software. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student has the habit to assess data quality and integrity. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - EC
| The student knows the societal relevance of statistics and data science. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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Selected topics in computational biology are covered in this course each academic year.
In the first half of the course, we will focus on machine learning algorithms for analysing biological data. A series of methods will be introduced for computational biology and bioinformatics. Relevant packages such as R and WinBUGS will be used.
In the second half of the course, the assumption is that conducting new clinical experiments is often very expensive. Therefore, systematic reviews and Meta-analysis of existing publications is increasingly becoming a useful approach to obtaining evidence in clinical research. Meta analysis can be done using R, REVMAN, CMA or such software, some of which are open source.
For AY 2022-2023, the course will cover the following topics:
- Machine learning methods in Bioinformatics
- Meta-analysis [Without emphasizing the systematic reviews component].
- Review of statistical methods in meta-analysis (Mantel Haenszel statistics, Peto Odds Ratio, Inverse Variance method, heterogeneity measures (chi-square, I square, ...), measures of agreement (McNemars, Kappa statistics))
- Meta-analysis using R software (OR, RR)
- Interpreting results using Forest plots, Funnel plots, heterogeneity statistics, etc.
- Quick Introduction to complex meta-analysis: Meta-regression, Network Meta-analysis using R.
- REVMAN software for meta-analysis, an open source software developed by COCHRAN COMMUNITY <https://community.cochrane.org/help/tools-and-software/revman-5>
This course is organized in cooperation with lecturers from our South partners.
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Lecture ✔
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Project ✔
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Paper ✔
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Presentation ✔
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Period 1 Credits 3,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 75 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Additional information | No participation at all in the assignments/projects will imply exclusion of participation in the final exam and incomplete participation will result in a reduced score of the final score. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Prerequisites |
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Corequisite for this course is basic knowledge of fundamental statistical concepts. Although no specific pre-requisites, this course will cover advanced statistical topics. |
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| Compulsory course material |
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Handouts made available by the instructors on Blackboard. |
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| 2nd year Master Bioinformatics | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 2nd year Master Biostatistics | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 2nd year Master Quantitative Epidemiology | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| Exchange Programme Statistics | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software. | | | - DC
| ... correctly using state-of-the-art analysis methodology. | | | - DC
| ... correctly using state-of-the-art software. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student has the habit to assess data quality and integrity. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - EC
| The student knows the societal relevance of statistics and data science. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
Selected topics in computational biology are covered in this course each academic year.
In the first half of the course, we will focus on machine learning algorithms for analysing biological data. A series of methods will be introduced for computational biology and bioinformatics. Relevant packages such as R and WinBUGS will be used.
In the second half of the course, the assumption is that conducting new clinical experiments is often very expensive. Therefore, systematic reviews and Meta-analysis of existing publications is increasingly becoming a useful approach to obtaining evidence in clinical research. Meta analysis can be done using R, REVMAN, CMA or such software, some of which are open source.
For AY 2022-2023, the course will cover the following topics:
- Machine learning methods in Bioinformatics
- Meta-analysis [Without emphasizing the systematic reviews component].
- Review of statistical methods in meta-analysis (Mantel Haenszel statistics, Peto Odds Ratio, Inverse Variance method, heterogeneity measures (chi-square, I square, ...), measures of agreement (McNemars, Kappa statistics))
- Meta-analysis using R software (OR, RR)
- Interpreting results using Forest plots, Funnel plots, heterogeneity statistics, etc.
- Quick Introduction to complex meta-analysis: Meta-regression, Network Meta-analysis using R.
- REVMAN software for meta-analysis, an open source software developed by COCHRAN COMMUNITY <https://community.cochrane.org/help/tools-and-software/revman-5>
This course is organized in cooperation with lecturers from our South partners.
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|
Lecture ✔
|
|
|
|
Project ✔
|
|
|
|
|
|
|
|
Paper ✔
|
|
|
|
Presentation ✔
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|
Period 1 Credits 3,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 75 % |
|
| Transfer of partial marks within the academic year | ✔ |
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|
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|
| Additional information | No participation at all in the assignments/projects will imply exclusion of participation in the final exam and incomplete participation will result in a reduced score of the final score. |
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
|
|
| Prerequisites |
| |
Corequisite for this course is basic knowledge of fundamental statistical concepts. Although no specific pre-requisites, this course will cover advanced statistical topics. |
|
|
| Compulsory course material |
| |
Handouts made available by the instructors on Blackboard. |
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1 examination regulations art.1.3, section 4. |
| 2 examination regulations art.4.7, section 2. |
3 examination regulations art.2.2, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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