Robust microarray meta-analysis identifies differentially expressed genes for clinical prediction.

ABSTRACT: Combining multiple microarray datasets increases sample size and leads to improved reproducibility in identification of informative genes and subsequent clinical prediction. Although microarrays have increased the rate of genomic data collection, sample size is still a major issue when identifying informative genetic biomarkers. Because of this, feature selection methods often suffer from false discoveries, resulting in poorly performing predictive models. We develop a simple meta-analysis-based feature selection method that captures the knowledge in each individual dataset and combines the results using a simple rank average. In a comprehensive study that measures robustness in terms of clinical application (i.e., breast, renal, and pancreatic cancer), microarray platform heterogeneity, a

SUBMITTER: Phan JH 

PROVIDER: S-EPMC3539384 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature