Logistic regression for disease classification using microarray data: model selection in a large p and small n case

Motivation: Logistic regression is a standard method for building prediction models for a binary outcome and has been extended for disease classification with microarray data by many authors. A feature (gene) selection step, however, must be added to penalized logistic modeling due to a large number...

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Bibliographic Details
Published in:	Bioinformatics Vol. 23; no. 15; pp. 1945 - 1951
Main Authors:	Liao, J.G., Chin, Khew-Voon
Format:	Journal Article
Language:	English
Published:	Oxford Oxford University Press 01.08.2007 Oxford Publishing Limited (England)
Subjects:	Algorithms Biological and medical sciences Biomarkers, Tumor - analysis Data Interpretation, Statistical Diagnosis, Computer-Assisted - methods Fundamental and applied biological sciences. Psychology General aspects Humans Leukemia Logistic Models Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Models, Biological Neoplasm Proteins - analysis Neoplasms - classification Neoplasms - diagnosis Neoplasms - metabolism Oligonucleotide Array Sequence Analysis - methods Prediction models Regression Analysis Reproducibility of Results Sample Size Sensitivity and Specificity Error estimation False positive Disease DNA chip Malignant tumor Gene expression Microarray Original document Logistic regression Computer program Classification Bootstrap Models Bioinformatics Cancer
ISSN:	1367-4803, 1367-4811, 1460-2059, 1367-4811
Online Access:	Get full text
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