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GEsnpx: A genetic ensemble approach for gene-gene interaction identification

  • Description

    GEsnpx is an implementation of a hybrid algorithm developed for gene-gene interaction identification in complex diseases. The system utilizes a multiple objective genetic algorithm with an ensemble of 5 nonlinear classifiers to capture gene-gene interaction through SNP markers. SNP subsets are evaluated and selected in a combinatorial manner, and potential interactions are identified by a combinatorial ranking procedure.

    Current version supports case-control designed association study. Besides its comparable detection power for SNP pair (two SNP interaction) to many other state-of-the-art programs, the parallel support of GEsnpx for higher-order gene-gene interaction identification set it aside from the single or pairwise based SNP screening algorithms. Please refer to reference [1] for more details on implementation and evaluation of GEsnpx.


  • News!

    Note that in current implementation, we have modified the classifier evaluation method using Area Under ROC Curve (AUC) to address the imbalanced case-control dataset. This may result in a longer computational time depending on the type of machines you are using. A random over sampling procedure is added to address the same problem when case-control ratio is highly imbalanced (need to be specified explicitly to use it). We expect those changes to increase the detection power when the data is imbalanced.

    A new diversity measure "kappa diversity" is implemented and used as default. The original "double fault diversity" can still be used by specifying through options.


  • Availability
    • GEsnpx 1.1 [download]
    • test dataset1 [20 SNPs] [download]
    • test dataset2 [100 SNPs] [download]
    • as requested by many people, the source code is now available for academic (non-commercial) users [download]

    The test dataset1 and test dataset2 are obtained from study [2].

    * Java 5.0 is required for executing the program.


    To obtain the general information about the program, run following command in command line (without parameters):

    java -jar GEsnpx.jar
    To test the program, run the program with the example dataset as follows:

    java -jar GEsnpx.jar -f balanced_200_0.2_20.arff
    To test the program verbosely, use the verbose option "-v" as follows:

    java -jar GEsnpx.jar -f balanced_200_0.2_20.arff -v


    We welcome any help on improving the quality of the software. To report bugs, please email to following address:



  • References
  • [1] Pengyi Yang, Joshua W.K. Ho, Albert Y. Zomaya, Bing B. Zhou, "A genetic ensemble approach for gene-gene interaction identification", BMC Bioinformatics, 2010, 11:524. [fulltext]
    [2] Jason H. Moore et al., "Application of genetic algorithms to the discovery of complex models for simulation studies in human genetics", In: Proceedings of the Genetic and Evolutionary Computation Conference, 1150-1155, 2002. [
    fulltext]