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PLoS ONE: Discovery and Expansion of Gene Modules by Seeking Isolated Groups in a Random Graph Process
We introduce a novel approach for the identification of modules based on the persistence of isolated gene groups within an evolving graph process. First, the underlying genomic data is summarized in the form of ranked gene–gene relationships, thereby accommodating studies that quantify the relevant biological relationship directly or indirectly. Then, the observed gene–gene relationship ranks are viewed as the outcome of a random graph process and candidate modules are given by the identifiable subgraphs that arise during this process. An isolation index is computed for each module, which quantifies the statistical significance of its survival time.
openbiomind - Google Code
OpenBiomind is a toolkit for analysis of gene expression, SNP and other biological datasets using advanced machine learning and pattern mining techniques, and includes traditional clustering, hybrid clustering, and other techniques.
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