Open-Source MDR 0.6.1
The Dartmouth Computational Genetics Laboratory is pleased to announce the release of version 0.6.1 BETA of our open-source Multifactor Dimensionality Reduction (MDR) software package.
Download information can be found here.
New features in MDR 0.6.1 include:
1) Search or wrapper algorithms
We have implemented the first of several different computational search or wrapper algorithms for identifying gene-gene interaction models when there are too many attributes (i.e. SNPs) to exhaustively evaluate. The first wrapper available is a random search. Future additions will include simulated annealing and a genetic algorithm, for example. A deterministic search strategy such as best first will also be added.
2) Additional filter algorithmsWe have added the cross-product odds ratio (OR) to the filter list. This new metric computes the OR for each pairwise combination of levels within an attribute. It also computes the OR comparing each level to all others combined. The largest OR is returned and can be used to filter SNPs for further analysis with MDR. Note that OR of infinity are not returned. Rather, a cell with a zero count is changed to 1 when the OR is computed. This puts the OR on a scale that is easier to plot and easier to compare across other attributes for the purpose of filtering. Additional filter metrics such as information gain will be added soon.
Download information can be found here.
New features in MDR 0.6.1 include:
1) Search or wrapper algorithms
We have implemented the first of several different computational search or wrapper algorithms for identifying gene-gene interaction models when there are too many attributes (i.e. SNPs) to exhaustively evaluate. The first wrapper available is a random search. Future additions will include simulated annealing and a genetic algorithm, for example. A deterministic search strategy such as best first will also be added.
2) Additional filter algorithmsWe have added the cross-product odds ratio (OR) to the filter list. This new metric computes the OR for each pairwise combination of levels within an attribute. It also computes the OR comparing each level to all others combined. The largest OR is returned and can be used to filter SNPs for further analysis with MDR. Note that OR of infinity are not returned. Rather, a cell with a zero count is changed to 1 when the OR is computed. This puts the OR on a scale that is easier to plot and easier to compare across other attributes for the purpose of filtering. Additional filter metrics such as information gain will be added soon.
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