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Penalized Discriminant Methods for the Classification of Tumors from Gene Expression Data
Author(s) -
Ghosh Debashis
Publication year - 2003
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2003.00114.x
Subject(s) - linear discriminant analysis , elastic net regularization , regression , partial least squares regression , microarray analysis techniques , ranking (information retrieval) , computer science , microarray databases , regression analysis , principal component regression , gene chip analysis , data mining , artificial intelligence , discriminant , dna microarray , principal component analysis , pattern recognition (psychology) , machine learning , mathematics , feature selection , statistics , gene , biology , gene expression , genetics
Summary . Due to the advent of high‐throughput microarray technology, it has become possible to develop molecular classification systems for various types of cancer. In this article, we propose a methodology using regularized regression models for the classification of tumors in microarray experiments. The performances of principal components, partial least squares, and ridge regression models are studied; these regression procedures are adapted to the classification setting using the optimal scoring algorithm. We also develop a procedure for ranking genes based on the fitted regression models. The proposed methodologies are applied to two microarray studies in cancer.