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)
Cancer Informatics has become an increasingly important source for research in the methodology of cancer genomics and the novel use of informatics technology. I have been impressed by the journal's contents and have been very gratified by the number of accesses to my recent publication. Cancer Informatics has filled an important gap in cancer research journals.