Data analysis tools
SigHunt: Horizontal gene transfer finder optimized for eukaryotic genomes
Authors: Kamil S. Jaroň, Jiří Moravec, Natália Martínková
The tool enables the estimation of two current survival measures (quantities) for the evaluation of chronic myeloid leukaemia (CML) patients’ disease status in time.
Data Analysis Tool for the Estimation of the Current Survival Measures
Authors: Eva Janoušová, Tomáš Pavlík, Richard Hůlek, Jiří Mayer, Ladislav Dušek
The tool enables the estimation of two current survival measures (quantities) for the evaluation of chronic myeloid leukaemia (CML) patients’ disease status in time.
DBM Toolbox for Neuroimage Data
Author: Daniel Schwarz
DBM Toolbox provides algorithms for deformable image registration of 3-D magnetic resonance brain images. The algorithms were implemented as functions and scripts in MATLAB® environment. Some of the functions, which provide computationally intensive tasks, have been compiled into *.mexw64 files for 64 bit Windows operation systems.
Recognition Toolbox for Neuroimage Data
Author: Eva Janoušová
Recognition Toolbox provides algorithms for reduction and classification of two-dimensional (2-D) or three-dimensional (3-D) medical image data acquired with diverse medical imaging techniques. The algorithms were implemented as functions in MATLAB® environment.
Penalised Reduction & Classification Toolbox
Author: Eva Janoušová
Penalised Reduction & Classification Toolbox provides algorithms for reduction and classification of various types of data, such as genetic data, two-dimensional (2-D) face image data or three-dimensional (3-D) brain image data. The algorithms were implemented as functions in MATLAB® environment. The toolbox enables reduction of data by selecting most discriminative features using penalised linear discriminant analysis with resampling, penalised linear regression with resampling, and t-test or feature extraction using intersubject principal component analysis.
Feature extraction & classification toolbox
Author: Radomír Kůs
FEATbox (Feature Extraction & clAssification Toolbox) is an outcome of attempts to compare feature extraction and selection methods for schizophrenia classification based on magnetic resonance images (MRI) of brains.
Toolbox for brain image recognition using artificial neural networks
Author: Roman Vyškovský
This toolbox is focused on brain image classification using artificial neural networks. The functions implemented in MATLAB® were invented during the experimentation, whose goal was to create a classification scheme that would be able to detect first-episode schizophrenia from images acquired from magnetic resonance device.
