Linear regression and two-class classification with gene expression data | Zendy

Xiaohong Huang | Zendy; Wei Pan | Zendy

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Linear regression and two-class classification with gene expression data

Author(s) -

Xiaohong Huang,

Wei Pan

Publication year - 2003

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/btg283

Subject(s) - class (philosophy) , regression , linear regression , computer science , expression (computer science) , computational biology , regression analysis , gene expression , gene , data mining , artificial intelligence , statistics , biology , genetics , mathematics , machine learning , programming language

Using gene expression data to classify (or predict) tumor types has received much research attention recently. Due to some special features of gene expression data, several new methods have been proposed, including the weighted voting scheme of Golub et al., the compound covariate method of Hedenfalk et al. (originally proposed by Tukey), and the shrunken centroids method of Tibshirani et al. These methods look different and are more or less ad hoc.

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