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Microarray Data Clustering Using Self-Organizing Maps

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dc.contributor.advisor Solano, Geoffrey A.
dc.contributor.author Marasigan, Zach Andrei V.
dc.date.accessioned 2015-07-27T05:57:59Z
dc.date.available 2015-07-27T05:57:59Z
dc.date.issued 2013-04
dc.identifier.uri http://cas.upm.edu.ph:8080/xmlui/handle/123456789/66
dc.description.abstract Microarray is one of the technologies used in the interdisciplinary science of BioInformatics. Its primary objective is to discover biological knowledge among genes through their expressions. Gene expressions usually come in large and multi-dimensional data which makes computational and statistical analyses necessary. Clustering of microarray data is one of these. Grouping similar genes together unfolds relationships of the biological properties of the genes under specific condition and, if supported by visualization, serves as good decision support for researchers. MaSOM is a software that uses Self-Organizing Maps, an Artificial Neural Network suitable both for clustering and for visualization, as its clustering method. This tool can be used to analyse large data set by preprocessing, clustering, and visualizing two-color cDNA microarray data. en_US
dc.language.iso en en_US
dc.subject Microarray en_US
dc.subject Gene Expression en_US
dc.subject Clustering en_US
dc.subject Self-Organizing Maps en_US
dc.title Microarray Data Clustering Using Self-Organizing Maps en_US
dc.type Thesis en_US


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