EDISA: extracting biclusters from multiple time-series of gene expression profiles-2

<p><b>Copyright information:</b></p><p>Taken from "EDISA: extracting biclusters from multiple time-series of gene expression profiles"</p><p>http://www.biomedcentral.com/1471-2105/8/334</p><p>BMC Bioinformatics 2007;8():334-334.</p><p>Published online 12 Sep 2007</p><p>PMCID:PMC2063505.</p><p></p>es (equation 14), if the respective value is lower than 0.15 no line is drawn. Table 1 provides an overview of all different module types

EDISA: extracting biclusters from multiple time-series of gene expression profiles-1

<p><b>Copyright information:</b></p><p>Taken from "EDISA: extracting biclusters from multiple time-series of gene expression profiles"</p><p>http://www.biomedcentral.com/1471-2105/8/334</p><p>BMC Bioinformatics 2007;8():334-334.</p><p>Published online 12 Sep 2007</p><p>PMCID:PMC2063505.</p><p></p>s of noise. The overlap of the implanted modules and the modules mined by EDISA were scored (equation 15). Six runs with 400 iterations were performed, with = 0.1 and = 0.2 for ∈ [0,0.5], = 0.15 for = 0.7 and = 0.2 for = 0.9

EDISA: extracting biclusters from multiple time-series of gene expression profiles-4

<p><b>Copyright information:</b></p><p>Taken from "EDISA: extracting biclusters from multiple time-series of gene expression profiles"</p><p>http://www.biomedcentral.com/1471-2105/8/334</p><p>BMC Bioinformatics 2007;8():334-334.</p><p>Published online 12 Sep 2007</p><p>PMCID:PMC2063505.</p><p></p>equation 14). If the respective value is lower than 0.15 no line is drawn. Table 1 provides an overview of all different module types

EDISA: extracting biclusters from multiple time-series of gene expression profiles-0

<p><b>Copyright information:</b></p><p>Taken from "EDISA: extracting biclusters from multiple time-series of gene expression profiles"</p><p>http://www.biomedcentral.com/1471-2105/8/334</p><p>BMC Bioinformatics 2007;8():334-334.</p><p>Published online 12 Sep 2007</p><p>PMCID:PMC2063505.</p><p></p>Here, we provide three predefined module types. Given this information random samples are drawn from the dataset (preprocessing). EDISA iteratively refines these samples and stores them if they match the module definition. After a specified number of runs EDISA computes the final modules (postprocessing)