Comput. Biol. Med. - Nonlinear dimensionality reduction of gene expression data for visualization and clustering analysis of cancer tissue samples.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ method(1969) cluster(1462) data(1082) }
{ analysi(2126) use(1163) compon(1037) }
{ data(3963) clinic(1234) research(1004) }
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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Gene expression data are the representation of nonlinear interactions among genes and environmental factors. Computing analysis of these data is expected to gain knowledge of gene functions and disease mechanisms. Clustering is a classical exploratory technique of discovering similar expression patterns and function modules. However, gene expression data are usually of high dimensions and relatively small samples, which results in the main difficulty for the application of clustering algorithms. Principal component analysis (PCA) is usually used to reduce the data dimensions for further clustering analysis. While PCA estimates the similarity between expression profiles based on the Euclidean distance, which cannot reveal the nonlinear connections between genes. This paper uses nonlinear dimensionality reduction (NDR) as a preprocessing strategy for feature selection and visualization, and then applies clustering algorithms to the reduced feature spaces. In order to estimate the effectiveness of NDR for capturing biologically relevant structures, the comparative analysis between NDR and PCA is exploited to five real cancer expression datasets. Results show that NDR can perform better than PCA in visualization and clustering analysis of complex gene expression data.

Resumo Limpo

gene express data represent nonlinear interact among gene environment factor comput analysi data expect gain knowledg gene function diseas mechan cluster classic exploratori techniqu discov similar express pattern function modul howev gene express data usual high dimens relat small sampl result main difficulti applic cluster algorithm princip compon analysi pca usual use reduc data dimens cluster analysi pca estim similar express profil base euclidean distanc reveal nonlinear connect gene paper use nonlinear dimension reduct ndr preprocess strategi featur select visual appli cluster algorithm reduc featur space order estim effect ndr captur biolog relev structur compar analysi ndr pca exploit five real cancer express dataset result show ndr can perform better pca visual cluster analysi complex gene express data

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