Data/hora: 16/06/2024 10:48:44

Provedor de dados: 	17	
País: 	United Kingdom	
Título: 	Association Analysis Techniques for Discovering Functional Modules from Microarray Data	
Autores: 	Gaurav Pandey; Gowtham Atluri; Michael Steinbach; Vipin Kumar.	
Data: 	2008-08-13	
Ano: 	2008	
Palavras-chave: 	Genetics &amp; Genomics; Molecular Cell Biology; Bioinformatics.	
Resumo: 	An application of great interest in microarray data analysis is the identification of a group of genes that show very similar patterns of expression in a data set, and are expected to represent groups of genes that perform common/similar functions, also known as functional modules. Although clustering offers a natural solution to this problem, it suffers from the limitation that it uses all the conditions to compare two genes, whereas only a subset of them may be relevant. Association analysis offers an alternative route for finding such groups of genes that may be co-expressed only over a subset of the experimental conditions used to prepare the data set. The techniques in this field attempt to find groups of data objects that contain coherent values across a set of attributes, in an exhaustive and efficient manner. In this paper, we illustrate how a generalization of the techniques in association analysis for real-valued data can be utilized to extract coherent functional modules from large microarray data sets.	
Tipo: 	Manuscript	
Identificador: 	http://precedings.nature.com/documents/2184/version/1

oai:nature.com:10.1038/npre.2008.2184.1

http://dx.doi.org/10.1038/npre.2008.2184.1	
Fonte: 	Nature Precedings	
Fonte: 	Creative Commons Attribution 3.0 License