MDR was developed to facilitate the detection, characterization, and interpretation of gene-gene interactions or epistasis. It uses a novel constructive induction algorithm to detect nonlinear interactions among discrete attributes.
What is MDR?
Multifactor Dimensionality Reduction (MDR) is a nonparametric and genetic model-free alternative to logistic regression for detecting and characterizing nonlinear interactions among discrete genetic and environmental attributes. The MDR method combines attribute selection, attribute construction, classification, cross-validation and visualization to provide a comprehensive and powerful data mining approach to detecting, characterizing, and interpreting nonlinear interactions.
Requirements:
· Java Runtime Environment (JRE) 1.5 or later
· Java HotSpot Client VM (build 1.4.2_06-b03, mixed mode)
What's New in This Release:
· Fix saved analysis output bug. In the '@results' section for each model, where the training and testing confusion matrix are output for each cross validation test, the testing was incorrect -- it was the training confusion matrix repeated rather than the testing matrix. The averages in the line with 'AvgTrain' were correct so the overall training and testing accuracies were still available.
· Fix bug where loading an analysis and then re-saving it would cause a null pointer exception.
· Simplest fix, although not ideal, was to disable save analysis when an old analysis is loaded.
· If a new analysis is run, then the 'Save Analysis' button will be enabled as usual. If a copy of an existing analysis is needed, it can be done outside of MDR.
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