A prognostic model for the combined analysis of gene expression profiling in hepatocellular carcinoma.

ABSTRACT: Microarray techniques using cDNA array and comparative genomic hybridization (CGH) have been developed for several discovery applications. They are frequently applied for the prediction and diagnosis of cancer in recent years. Many studies have shown that integrating genomic data from different sources may increase the reliability of gene expression analysis results in understanding cancer progression. Therefore, developing a good prognostic model dealing simultaneously with different types of dataset is important. The challenge with these types of data is high background noise. We describe an analytical two-stage framework with a multi-parallel data analysis method named wavelet-based generalized singular value decomposition and shaving method (WGSVD-shaving). This method is proposed for

SUBMITTER: Benny ZC 

PROVIDER: S-EPMC2532705 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature