Independent component analysis-based penalized discriminant method for tumor classification using gene expression data | Zendy

De-Shuang Huang | Zendy; Chun-Hou Zheng | Zendy

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Independent component analysis-based penalized discriminant method for tumor classification using gene expression data

Author(s) -

De-Shuang Huang,

Chun-Hou Zheng

Publication year - 2006

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btl190

Subject(s) - computer science , data mining , dna microarray , statistic , linear discriminant analysis , microarray analysis techniques , expression (computer science) , gene chip analysis , artificial intelligence , component (thermodynamics) , machine learning , pattern recognition (psychology) , gene expression , gene , statistics , mathematics , biology , biochemistry , physics , programming language , thermodynamics

Microarrays are capable of determining the expression levels of thousands of genes simultaneously. One important application of gene expression data is classification of samples into categories. In combination with classification methods, this technology can be useful to support clinical management decisions for individual patients, e.g. in oncology. Standard statistic methodologies in classification or prediction do not work well when the number of variables p (genes) far too exceeds the number of samples n. So, modification of existing statistical methodologies or development of new methodologies is needed for the analysis of microarray data.

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