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To further test this hypothesis, we used the previously described "gene module map method" [24] to identify significantly enriched cytobands in the AE microarray data set.

Significant enrichment of AE-associated genes (P<0.05; corrected for multiple hypotheses using FDR) was determined using the "gene module map method" implemented in Genomica [24].

Using the "gene module map method" [24] to identify significantly enriched Gene Ontology (GO) terms [25], we found that "immune response" was the most significantly enriched GO term in up-regulated AE genes (P<10−73) and "lipid metabolism" was the most significantly enriched GO term in down-regulated AE genes (P<10−33) (full list of enriched GO terms is provided in Supplementary Table S2).

For instance, the well-known meta-analysis module map method (Segal et al., 2004) does not include an approach for computing the relevance between studies.

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We used the module-map method as implemented in the software package Genomica (available at http://genomica.weizmann.ac.il) using the log-ratio compendium and default settings.

The map method can calculate descriptors in the CPU parallel using the multiprocessing module in the standard Python library (Code 5).

A comparison to the results of Wong et al. (2008) [6] demonstrated that the homolog modules were enriched for modules of higher recurrence than the modules reported in the Stemness Module Map (Figure S3).

Cancer module map information was downloaded from the web browser (http://ai.stanford.edu/~erans/cancer/).edu/~erans/cancer/

From the Handler Mappings Actions panel, click on Add Module Mapping.

Other applications given here are to extension problems for module maps, and characterizations of C*-algebras.

We also give a general theorem concerning the automatic complete boundedness of module maps.

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