Lehrinhalte
[list]
[*]Elementary methods of machine learning: Regression, classification, clustering (probabilistic graphical models)
[*]Analysis and visualization of high-dimensional data (multi-dimensional scaling, principal component analysis, embedding methods with deep neural networks, tSNE, UMAP)
[*]Data-driven reconstruction of molecular interaktion networks (Bayes nets, solution to Gausian graphical models, Causality analysis)
[*]Analysis of interaction networks (modularity, graph partitioning, spanning trees, differential networks, network motifs, STRING database, PathBLAST)
[*]Dynamical models of molecular interaction networks (stochastic Markov-modes, differential equations, Reaction rate equation)
[*]Elementary algorithms for structure determination of proteins and RNAs (Secondary structure prediction of RNAs, molecular dynamics, common simulators and force fields)
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Voraussetzungen
Bioinformatics I
[list]
[*]Elementary methods of machine learning: Regression, classification, clustering (probabilistic graphical models)
[*]Analysis and visualization of high-dimensional data (multi-dimensional scaling, principal component analysis, embedding methods with deep neural networks, tSNE, UMAP)
[*]Data-driven reconstruction of molecular interaktion networks (Bayes nets, solution to Gausian graphical models, Causality analysis)
[*]Analysis of interaction networks (modularity, graph partitioning, spanning trees, differential networks, network motifs, STRING database, PathBLAST)
[*]Dynamical models of molecular interaction networks (stochastic Markov-modes, differential equations, Reaction rate equation)
[*]Elementary algorithms for structure determination of proteins and RNAs (Secondary structure prediction of RNAs, molecular dynamics, common simulators and force fields)
[/list]
Voraussetzungen
Bioinformatics I
- Lehrende: Heinz Köppl
Semester: Inverno 2022/23