SGN-54106 Computational Diagnostics, 5 cr
Additional information
Suitable for postgraduate studies.
Person responsible
Frank Emmert-Streib, Susanna Ketola
Lessons
| Implementation | Period | Person responsible | Requirements |
| SGN-54106 2019-01 | 4 |
Frank Emmert-Streib Aliyu Musa |
To complete the course, the student is required to (all three requirements must be completed to pass the course): a) Execute the project work (20% of the final grade) b) Execute the weekly exercises (1 per exercises lesson, 40% of the final grade) c) Do the final exam (40% of the final grade) |
Learning Outcomes
After completing the course, the student gained a basic understanding of the definition and the meaning of computational diagnostics and its utility for biomedical research and business intelligence. Case studies will be discussed illustrating the interplay between computational and statistical methods that are applied to large-scale and high-dimensional data sets from genomic experiments and business processes, e.g., the stock market or consumer behavior. Moreover, the student will learn how to practically approach such problems by using the statistical programming language R. In general, the course teaches statistical thinking in the context of biomedical and business problems, i.e., the adaptation of machine learning methods in a problem specific manner.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Classification of disease groups | Computational implementation and interpretation; classification methods | |
| 2. | Business intelligence | Data analysis methods for business processes | |
| 3. | Survival analysis | Regression models for time-to event processes | |
| 4. | Genomics data | Preprocessing and normalization of gene expression data from microarray experiments | |
| 5. | Programming in R | Usage and programming in the statistical programming language R | |
| 6. | Quantitative assessment of results | Statistical error measures; resampling techniques | |
| 7. | Predictive models | Linear regression, hypothesis testing; general models in data science |
Instructions for students on how to achieve the learning outcomes
To complete the course, the student is required to (all three requirements must be completed to pass the course): a) Execute the project work (20% of the final grade) b) Execute the weekly exercises (1 per exercises lesson, 40% of the final grade) c) Do the final exam (40% of the final grade)
Assessment scale:
Numerical evaluation scale (0-5)
Partial passing:
Study material
| Type | Name | Author | ISBN | URL | Additional information | Examination material |
| Book | An Introduction to Statistical Learning | Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani | No | |||
| Book | Statistics and Data Analysis for Microarrays Using R and Bioconductor | Sorin Dr | Introduction to the analysis of microarray data. | No |
Additional information about prerequisites
Basic programming skills. Experience with the language R are desirable, but not necessary. Basic knowledge in Mathematics and Machine Learning. Basic knowledge of biology/systems biology.
Correspondence of content
| Course | Corresponds course | Description |
| SGN-54106 Computational Diagnostics, 5 cr | BMT-53007 Computational Diagnostics, 5 cr |