Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networks.

ABSTRACT:

Motivation

Understanding and predicting an individual's response in a clinical trial is the key to better treatments and cost-: effective medicine. Over the coming years, more and more large-scale omics datasets will become available to characterize patients with complex and heterogeneous diseases at a molecular level. Unfortunately, genetic, phenotypical and environmental variation is much higher in a human trial population than currently modeled or measured in most animal studies. In our experience, this high variability can lead to failure of trained predictors in independent studies and undermines the credibility and utility of promising high-dimensional datasets.

Methods

We propose a method that utilizes patient-level genome-wide expression data in conjunction with caus

SUBMITTER: Zarringhalam K 

PROVIDER: S-EPMC4058945 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature