%0 Journal Article
%T Bayesian profiling of molecular signatures to predict event times
%A Dabao Zhang
%A Min Zhang
%J Theoretical Biology and Medical Modelling
%D 2007
%I BioMed Central
%R 10.1186/1742-4682-4-3
%X 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 Bayesian approach is developed to rank the centroids of these linkage clusters and provide a list of the main molecular features closely related to the outcome of interest. A simulation study showed the superior performance of our approach. When it was applied to data on diffuse large B-cell lymphoma (DLBCL), we were able to identify some new candidate signatures for disease prognosis.This multivariate approach provides researchers with a more reliable list of molecular features profiled in terms of their prognostic relationship to the event times, and generates dependable information for subsequent identification of prognostic molecular signatures through either biological procedures or further data analysis.High-throughput biotechnologies such as microarray and mass spectrometry permit simultaneous measurements of enormous bodies of genomic, proteomic, and metabolic information to be made. Such information helps us understand the molecular basis of important clinical outcomes, and thus improves the efficiency as well as accuracy in clinical decision making. More specifically, a small subset of these molecules can be used as biomarkers in daily clinical practice for detecting disease at early stages, measuring disease progress, monitoring the efficacy of treatments, and potentially accelerating the drug discovery process. However, the promise of genomics, proteomics, and metabolomics in clinical medicine rests on identifying these disease-specific molecular signatures. Clinical and preclinical studies of patients' genomics and proteomics profiles usually present datasets that share common characteristics, i.e., many molecular features ("large p") collected from few individuals ("small n"). The statist
%U http://www.tbiomed.com/content/4/1/3