Open-Source MDR 1.0.0rc
The Dartmouth Computational Genetics Laboratory is pleased to announce the release of version 1.0.0rc1 of our multifactor dimensionality reduction (MDR) software package. This is the first release candidate (rc1) for version 1.0. This new version includes several important new features along with a number of minor tweaks and fixes. We will beta test this version over the next few weeks. Please send us your feedback and suggestions.
Download Open-Source MDR 1.0.0rc1 here.
Major New Features:
1) Attribute construction. At the heart of MDR is a contructive induction algorithm that takes two or more SNPs and creates a new attribute that is inserted into the dataset. The goal here is to change the representation space of the data to make interaction easier to detect using any statistical or computational classifier (e.g. naive Bayes, logistic regression, decision trees, etc.). The new attribute construction tab allows the user to select two or more variables and construct a new single variable that is inserted into the dataset. This new dataset can then be analyzed using MDR or exported to other analysis software packages (e.g. R, SAS, SPSS, Weka). This may also be useful for modeling hierarchical epistasis. For more information about attribute construction please see our new paper in the Journal of Theoretical Biology [PubMed].
2) Interaction dendrograms. This new feature was added to the MDR software to facilitate statistical interpretation of MDR models. This is accomplished using estimates of interaction information (entropy-based measures) to measure the amount of information about the class (e.g. case-control status) that is gained by putting two attributes together using MDR. Here, a distance matrix is estimated using these entropy measures which in turn are used to build a dendrogram using hierarchical cluster analysis. These dendrograms indicate the degree of synergy or redundancy of pairs of attributes. Red lines in the dendrogram indicate synergy while blue lines indicate redundancy or correlation. Interaction dendrograms have been described previously using Cartersian products by Dr. Aleks Jakulin [PDF] and are described in our new paper in the Journal of Theoretical Biology [PubMed].
This work is supported by NIH grant AI59695
Download Open-Source MDR 1.0.0rc1 here.
Major New Features:
1) Attribute construction. At the heart of MDR is a contructive induction algorithm that takes two or more SNPs and creates a new attribute that is inserted into the dataset. The goal here is to change the representation space of the data to make interaction easier to detect using any statistical or computational classifier (e.g. naive Bayes, logistic regression, decision trees, etc.). The new attribute construction tab allows the user to select two or more variables and construct a new single variable that is inserted into the dataset. This new dataset can then be analyzed using MDR or exported to other analysis software packages (e.g. R, SAS, SPSS, Weka). This may also be useful for modeling hierarchical epistasis. For more information about attribute construction please see our new paper in the Journal of Theoretical Biology [PubMed].
2) Interaction dendrograms. This new feature was added to the MDR software to facilitate statistical interpretation of MDR models. This is accomplished using estimates of interaction information (entropy-based measures) to measure the amount of information about the class (e.g. case-control status) that is gained by putting two attributes together using MDR. Here, a distance matrix is estimated using these entropy measures which in turn are used to build a dendrogram using hierarchical cluster analysis. These dendrograms indicate the degree of synergy or redundancy of pairs of attributes. Red lines in the dendrogram indicate synergy while blue lines indicate redundancy or correlation. Interaction dendrograms have been described previously using Cartersian products by Dr. Aleks Jakulin [PDF] and are described in our new paper in the Journal of Theoretical Biology [PubMed].
This work is supported by NIH grant AI59695
posted by Jason H. Moore, Ph.D. at
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