Maximum predictive power of the microarray-based models for clinical outcomes is limited by correlation between endpoint and gene expression profile.

ABSTRACT:

Background

Microarray data have been used for gene signature selection to predict clinical outcomes. Many studies have attempted to identify factors that affect models' performance with only little success. Fine-tuning of model parameters and optimizing each step of the modeling process often results in over-fitting problems without improving performance.

Results

We propose a quantitative measurement, termed consistency degree, to detect the correlation between disease endpoint and gene expression profile. Different endpoints were shown to have different consistency degrees to gene expression profiles. The validity of this measurement to estimate the consistency was tested with significance at a p-value less than 2.2e-16 for all of the studied endpoints. According to the con

SUBMITTER: Zhao C 

PROVIDER: S-EPMC3287499 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature