Dimension reduction-based penalized logistic regression for cancer classification using microarray data.
The use of penalized logistic regression for cancer classification using microarray expression data is presented. Two dimension reduction methods are respectively combined with the penalized logistic regression so that both the classification accuracy and computational speed are enhanced. Two other machine-learning methods, support vector machines and least-squares regression, have been chosen for comparison. It is shown that our methods have achieved at least equal or better results. They also have the advantage that the output probability can be explicitly given and the regression coefficients are easier to interpret. Several other aspects, such as the selection of penalty parameters and components, pertinent to the application of our methods for cancer classification are also discussed.
Shen
Li
L
BioInformatics Research Centre, Nanyang Technological University, Research TechnoPlaza, 3rd Story, XFrontiers Block, 50 Nanyang Drive, Singapore 637553, Singapore. shenli@pmail.ntu.edu.sg
Tan
Eng Chong
EC
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D008957:Models, Genetic; D009363:Neoplasm Proteins / Q000032:analysis*; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins