أعرض تسجيلة المادة بشكل مبسط

dc.creator Carmona-Sáez, Pedro
dc.creator Chagoyen, Mónica
dc.creator Rodríguez, Andrés
dc.creator Trelles, Oswaldo
dc.creator Carazo, José M.
dc.creator Pascual-Montano, Alberto
dc.date 2007-05-08T13:45:45Z
dc.date 2007-05-08T13:45:45Z
dc.date 2006-02-07
dc.date.accessioned 2017-01-31T00:57:10Z
dc.date.available 2017-01-31T00:57:10Z
dc.identifier BMC Bioinformatics 2006, 7:54
dc.identifier 1471-2105
dc.identifier http://hdl.handle.net/10261/1421
dc.identifier 10.1186/1471-2105-7-54
dc.identifier.uri http://dspace.mediu.edu.my:8181/xmlui/handle/10261/1421
dc.description This article is available from: http://www.biomedcentral.com/1471-2105/7/54
dc.description [Background] Microarray technology is generating huge amounts of data about the expression level of thousands of genes, or even whole genomes, across different experimental conditions. To extract biological knowledge, and to fully understand such datasets, it is essential to include external biological information about genes and gene products to the analysis of expression data. However, most of the current approaches to analyze microarray datasets are mainly focused on the analysis of experimental data, and external biological information is incorporated as a posterior process.
dc.description [Results] In this study we present a method for the integrative analysis of microarray data based on the Association Rules Discovery data mining technique. The approach integrates gene annotations and expression data to discover intrinsic associations among both data sources based on co-occurrence patterns. We applied the proposed methodology to the analysis of gene expression datasets in which genes were annotated with metabolic pathways, transcriptional regulators and Gene Ontology categories. Automatically extracted associations revealed significant relationships among these gene attributes and expression patterns, where many of them are clearly supported by recently reported work.
dc.description [Conclusion] The integration of external biological information and gene expression data can provide insights about the biological processes associated to gene expression programs. In this paper we show that the proposed methodology is able to integrate multiple gene annotations and expression data in the same analytic framework and extract meaningful associations among heterogeneous sources of data. An implementation of the method is included in the Engene software package.
dc.description Peer reviewed
dc.language eng
dc.publisher BioMed Central
dc.relation Publisher’s version
dc.rights openAccess
dc.title Integrated analysis of gene expression by association rules discovery
dc.type Artículo


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أعرض تسجيلة المادة بشكل مبسط