Poslední úprava: Lukasz Cwiklik (29.03.2019)
* Syllabus:
l Overview of computational strategies applied to cell and macromolecular imaging (4 lectures)
- Introduction to pattern and morphology recognition (½ lecture)
(cases studies in cell confocal imaging and CRYOEM image sorting approaches)
-Image manipulation methods (½ lecture)
(benefits and drawbacks of image manipulation, filtering techniques, and pixel normalization)
-Concepts of probability theory applied to pattern recognition (1 lectures)
(probability densities, expectations and covariances, Bayesian probabilities, the Gaussian distribution, curve fitting, and Bayesian curve fitting)
-Choosing an appropriate model for a given biological system and phenomenon (½ lecture)
(minimizing the number of model parameters, Markov state models, finite elements, and Voronoi tessellation)
-Decision theory as methodology behind pattern and morphology recognition techniques (1 lectures)
(minimizing the misclassification rate, minimizing the expected loss, the reject option, inference and decision, and loss functions for regression)
-Machine learning applied to image recognition (½ lecture)
l Data mining and high-throughput data analysis (1 lecture)
-Introduction and methods
(case study on DNA search patterns; Association Analysis for Large-Scale Gene Set Data)
l Overview of computational methods in biological problems: from atomistic to macroscopic approaches (6 lectures)
-Modeling biosystems at atomistic resolution (2 lecture)
(choosing an appropriate molecular-level approach: quantum mechanics, molecular dynamics, Monte Carlo, and QM/MM)
-Modeling of mesoscopic and macroscopic biological systems (2 lectures)
(mean field models; thermodynamic and kinetic models)
-Network analysis and kinetics in biosystems (2 lectures)
(signaling, transport, and biochemical cycles)
l Software and computational tools in computational biology (3 lectures)
-General programming platforms optimal for biological problems (½ lecture)
(Python, R, and Matlab/Octave)
-Image processing software (1 lecture)
(ImageJ, and matplotlib)
-Choosing a proper software for a particular computational biological problem (½ lecture)
(computational strategies in connection with limitations of the theory and methods behind)
-Machine learning (1 lecture)
(TensorFlow, and scikit-learn)
Poslední úprava: doc. RNDr. Iva Zusková, CSc. (02.05.2017)
Syllabus:
l Overview of computational strategies applied to cell and macromolecular imaging (4 lectures)
Introduction to pattern and morphology recognition (½ lecture)
(cases studies in cell confocal imaging and CRYOEM image sorting approaches)
Image manipulation methods (½ lecture)
(benefits and drawbacks of image manipulation, filtering techniques, and pixel normalization)
Concepts of probability theory applied to pattern recognition (1 lectures)
(probability densities, expectations and covariances, Bayesian probabilities, the Gaussian distribution, curve fitting, and Bayesian curve fitting)
Choosing an appropriate model for a given biological system and phenomenon (½ lecture)
(minimizing the number of model parameters, Markov state models, finite elements, and Voronoi tessellation)
Decision theory as methodology behind pattern and morphology recognition techniques (1 lectures)
(minimizing the misclassification rate, minimizing the expected loss, the reject option, inference and decision, and loss functions for regression)
Machine learning applied to image recognition (½ lecture)
l Data mining and high-throughput data analysis (1 lecture)
Introduction and methods
(case study on DNA search patterns; Association Analysis for Large-Scale Gene Set Data)
l Overview of computational methods in biological problems: from atomistic to macroscopic approaches (6 lectures)
Modeling biosystems at atomistic resolution (2 lecture)
(choosing an appropriate molecular-level approach: quantum mechanics, molecular dynamics, Monte Carlo, and QM/MM)
Modeling of mesoscopic and macroscopic biological systems (2 lectures)
(mean field models; thermodynamic and kinetic models)
Network analysis and kinetics in biosystems (2 lectures)
(signaling, transport, and biochemical cycles)
l Software and computational tools in computational biology (3 lectures)
General programming platforms optimal for biological problems (½ lecture)
(Python, R, and Matlab/Octave)
Image processing software (1 lecture)
(ImageJ, and matplotlib)
Choosing a proper software for a particular computational biological problem (½ lecture)
(computational strategies in connection with limitations of the theory and methods behind)
Machine learning (1 lecture)
(TensorFlow, and scikit-learn)
