Reproducible cancer biomarker discovery in SELDI-TOF MS using different pre-processing algorithms.

ABSTRACT:

Background

There has been much interest in differentiating diseased and normal samples using biomarkers derived from mass spectrometry (MS) studies. However, biomarker identification for specific diseases has been hindered by irreproducibility. Specifically, a peak profile extracted from a dataset for biomarker identification depends on a data pre-processing algorithm. Until now, no widely accepted agreement has been reached.

Results

In this paper, we investigated the consistency of biomarker identification using differentially expressed (DE) peaks from peak profiles produced by three widely used average spectrum-dependent pre-processing algorithms based on SELDI-TOF MS data for prostate and breast cancers. Our results revealed two important factors that affect the consisten

SUBMITTER: Zou J 

PROVIDER: S-EPMC3194809 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature