Regularized binormal ROC method in disease classification using microarray data.

ABSTRACT:

Background

An important application of microarrays is to discover genomic biomarkers, among tens of thousands of genes assayed, for disease diagnosis and prognosis. Thus it is of interest to develop efficient statistical methods that can simultaneously identify important biomarkers from such high-throughput genomic data and construct appropriate classification rules. It is also of interest to develop methods for evaluation of classification performance and ranking of identified biomarkers.

Results

The ROC (receiver operating characteristic) technique has been widely used in disease classification with low dimensional biomarkers. Compared with the empirical ROC approach, the binormal ROC is computationally more affordable and robust in small sample size cases. We propose usin

SUBMITTER: Ma S 

PROVIDER: S-EPMC1513612 | biostudies-literature | 2006 May

REPOSITORIES: biostudies-literature