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NCR-PCOPGene: An Exploratory Tool for Analysis of Sample-Classes Effect on Gene-Expression Relationships

Background. Microarray technology is so expensive and powerful that it is essential to extract maximum value from microarray data. Our tools allow researchers to test and formulate from a hypothesis to entire models. Results. The objective of the NCRPCOPGene is to study the relationships among gene...

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Detalles Bibliográficos
Autores principales: Cedano, Juan, Huerta, Mario, Querol, Enrique
Formato: Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2775662/
https://www.ncbi.nlm.nih.gov/pubmed/19920990
http://dx.doi.org/10.1155/2008/789026
Descripción
Sumario:Background. Microarray technology is so expensive and powerful that it is essential to extract maximum value from microarray data. Our tools allow researchers to test and formulate from a hypothesis to entire models. Results. The objective of the NCRPCOPGene is to study the relationships among gene expressions under different conditions, to classify these conditions, and to study their effect on the different relationships. The web application makes it easier to define the sample classes, grouping the microarray experiments either by using (a) biological, statistical, or any other previous knowledge or (b) their effect on the expression relationship maintained among specific genes of interest. By means of the type (a) class definition, the researcher can add biological information to the gene-expression relationships. The type (b) class definition allows for linking genes correlated neither linearly nor nonlinearly. Conclusions. The PCOPGene tools are especially suitable for microarrays with large sample series. This application helps to identify cellular states and the genes involved in it in a flexible way. The application takes advantage of the ability of our system to relate gene expressions; even when these relationships are noncontinuous and cannot be found using linear or nonlinear analytical methods.