Gene selection in cancer classification using sparse logistic regression with Bayesian regularization

• Published in: Bioinformatics Vol. 22; no. 19; pp. 2348 - 2355
• Main Authors: Cawley, Gavin C., Talbot, Nicola L. C.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.10.2006; Oxford Publishing Limited (England)
• Subjects: Algorithms; Bayes Theorem; Biological and medical sciences; Biomarkers, Tumor - analysis; Classification; Diagnosis, Computer-Assisted - methods; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling - methods; 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; Regression Analysis; Reproducibility of Results; Sensitivity and Specificity; MATLAB software; Selection; Malignant tumor; Gene expression; Vector method; Algorithm; Original document; Logistic regression; Regulation(control); Gene; Classification; Bayes network; Bioinformatics

Motivation: Gene selection algorithms for cancer classification, based on the expression of a small number of biomarker genes, have been the subject of considerable research in recent years. Shevade and Keerthi propose a gene selection algorithm based on sparse logistic regression (SLogReg) incorpor...