Course detail
Multidimensional Analysis of Biomedical Data
FEKT-MPC-VMMAcad. year: 2022/2023
Not applicable.
Language of instruction
Czech
Number of ECTS credits
5
Mode of study
Not applicable.
Guarantor
Learning outcomes of the course unit
Not applicable.
Prerequisites
Not applicable.
Co-requisites
Not applicable.
Planned learning activities and teaching methods
Not applicable.
Assesment methods and criteria linked to learning outcomes
Not applicable.
Course curriculum
1. Introduction to analysis of multidimentional biological data. Multidimentional analysis object, pros and cons. Classification of the methods.
2. Linear algebra foundations.
3. Multidimentional distributions and statistical tests.
4. Methods for data preprocessing. Transformation and standardization approaches. Problem of missing data.
5. Relationship between variables in multidimentional space. Similarity and distance measures. Correlation and covariance.
6. Cluster analysis of biological data. Hierarchical and non-hierarchical clustering. Determining the optimal number of clusters. Clusters validation.
7. Ordinal analysis. Review of the methods used in biomedical applications.
8. Principal component analysis (PCA). Singular value decomposition.
9. Factor analysis. Fundamentals of factor analysis. Rotation of the factors.
10. Independent component analysis (ICA). ICA based feature extraction from biomedical data. Relationship between PCA, ICA and factor analysis.
11. Non-linear methods for data dimensionality reduction.
12. Multidimensional data analysis in biomedicine applications – overview.
2. Linear algebra foundations.
3. Multidimentional distributions and statistical tests.
4. Methods for data preprocessing. Transformation and standardization approaches. Problem of missing data.
5. Relationship between variables in multidimentional space. Similarity and distance measures. Correlation and covariance.
6. Cluster analysis of biological data. Hierarchical and non-hierarchical clustering. Determining the optimal number of clusters. Clusters validation.
7. Ordinal analysis. Review of the methods used in biomedical applications.
8. Principal component analysis (PCA). Singular value decomposition.
9. Factor analysis. Fundamentals of factor analysis. Rotation of the factors.
10. Independent component analysis (ICA). ICA based feature extraction from biomedical data. Relationship between PCA, ICA and factor analysis.
11. Non-linear methods for data dimensionality reduction.
12. Multidimensional data analysis in biomedicine applications – overview.
Work placements
Not applicable.
Aims
Not applicable.
Specification of controlled education, way of implementation and compensation for absences
Not applicable.
Recommended optional programme components
Not applicable.
Prerequisites and corequisites
Not applicable.
Basic literature
D. Haruštiaková, J. Jarkovský, S. Littnerová, L. Dušek: Vícerozměrné statistické metody v biologii, CERM 2012 (CS)
J. Holčík: Analýza a klasifikace dat, CERM 2012 (CS)
M. Meloun, J. Militký: Kompendium statistického zpracování dat, Academia 2006 (CS)
Meloun M. a kol.: Statistická analýza vícerozměrných dat v příkladech, 2017, Karolinum, 978-80-246-3618-4
J. Holčík: Analýza a klasifikace dat, CERM 2012 (CS)
M. Meloun, J. Militký: Kompendium statistického zpracování dat, Academia 2006 (CS)
Meloun M. a kol.: Statistická analýza vícerozměrných dat v příkladech, 2017, Karolinum, 978-80-246-3618-4
Recommended reading
A. Hyvärinen, J. Karhunen, E. Oja: Independent Component Analysis, Wiley 2001 (CS)
M. Kovár: Maticový a tenzorový počet, VUT v Brně (CS)
M. Kovár: Maticový a tenzorový počet, VUT v Brně (CS)
Elearning
eLearning: currently opened course
Classification of course in study plans
Type of course unit
Computer-assisted exercise
26 hod., compulsory
Teacher / Lecturer
Elearning
eLearning: currently opened course