Sign up for email alerts to receive notifications of new articles published in Cancer Informatics
There have been relatively few publications using linear regression models to predict a continuous response based on microarray expression profiles. Standard linear regression methods are problematic when the number of predictor variables exceeds the number of cases. We have evaluated three linear regression algorithms that can be used for the prediction of a continuous response based on high dimensional gene expression data. The three algorithms are the least angle regression (LAR), the least absolute shrinkage and selection operator (LASSO), and the averaged linear regression method (ALM). All methods are tested using simulations based on a real gene expression dataset and analyses of two sets of real gene expression data and using an unbiased complete cross validation approach. Our results show that the LASSO algorithm often provides a model with somewhat lower prediction error than the LAR method, but both of them perform more efficiently than the ALM predictor. We have developed a plug-in for BRB-ArrayTools that implements the LAR and the LASSO algorithms with complete cross-validation.
PDF (644.81 KB PDF FORMAT)
RIS citation (ENDNOTE, REFERENCE MANAGER, PROCITE, REFWORKS)
BibTex citation (BIBDESK, LATEX)
My experience working with the staff and publishing in Cancer Informatics has been fantastic. As a new contributor to the journal, I thought the production coordinators and editors were extremely helpful in guiding me through the publication process in a smooth and efficient manner. Furthermore, I enjoyed how easy it was to track our manuscript throughout the process. The review process was prompt, yet thorough, and provided us with excellent feedback to improve the clarity ...