Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học quốc tế cung cấp cho các bạn kiến thức về ngành y đề tài: " Bayesian profiling of molecular signatures to predict event times | Theoretical Biology and Medical Modelling BioMed Central Research Open Access Bayesian profiling of molecular signatures to predict event times Dabao Zhang and Min Zhang Address Department of Statistics Purdue University 150 N. University Street West Lafayette Indiana 47907-2067 USA Email Dabao Zhang - zhangdb@ Min Zhang - minzhang@ Corresponding author Published 19 January 2007 Received 24 September 2006 Theoretical Biology and Medical Modelling 2007 4 3 doi 1742-4682-4-3 Accepted 19 January 2007 This article is available from http content 4 1 3 2007 Zhang and Zhang licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 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 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
