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EpiMiner

 Detecting and visualizing nonlinear interactive effects of Single Nucleotide Polymorphisms (SNPs) or epistatic interactions are important topics of signal processing having great mathematical and computational challenges. To address these problems, a three-stage method, epiMiner (epistasis Miner), is proposed based on co-information theory. In screening stage, Co-Information Index (CII) is employed to visualize and rank contributions of individual SNPs to the phenotype, with the number of top ranking SNPs retained to next stage specified by users directly or a support vector machine classifier automatically. In testing stage, co-information and co-information based permutation test are conducted sequentially to search epistatic interactions within the retained SNPs, with the results then ranked by their p-values. For further characterizing broader epistasis landscape, a visualizing stage is designed to dynamically construct epistasis networks by linking pairs of the retained SNPs if their co-information values with respect to the phenotype are stronger than thresholds. The performance of epiMiner is compared with existing methods on a diverse range of simulated data sets containing several epistasis models. Results demonstrate that epiMiner is effective in detecting and visualizing epistatic interactions. In addition, the application of epiMiner on a real Age-related Macular Degeneration (AMD) data set provides several new clues for the exploration of causative factors of AMD. The Matlab version of epiMiner software is available free online at https://sourceforge.net/projects/epiminer/files/.



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