Bayesian profiling of molecular signatures to predict event times.

ABSTRACT:

Background

It is of particular interest to identify cancer-specific molecular signatures for early diagnosis, monitoring effects of treatment and predicting patient survival time. Molecular information about patients is usually generated from high throughput technologies such as microarray and mass spectrometry. Statistically, we are challenged by the large number of candidates but only a small number of patients in the study, and the right-censored clinical data further complicate the analysis.

Results

We present a two-stage procedure to profile molecular signatures for survival outcomes. Firstly, we group closely-related molecular features into linkage clusters, each portraying either similar or opposite functions and playing similar roles in prognosis; secondly, a Bayesia

SUBMITTER: Zhang D 

PROVIDER: S-EPMC1796541 | biostudies-literature | 2007 Jan

REPOSITORIES: biostudies-literature