Use of Bayesian networks to probabilistically model and improve the likelihood of validation of microarray findings by RT-PCR

作     者：English, Sangeeta B. Shih, Shou-Ching Ramoni, Marco F. Smith, Lois E. Butte, Atul J. 

作者机构：Stanford Univ Sch Med Stanford Ctr Biomed Informat Res BMIR Stanford CA 94305 USA Beth Israel Deaconess Med Ctr Res N Dept Pathol Boston MA 02215 USA Childrens Hosp Boston Dept Ophthalmol Boston MA 02115 USA Harvard Univ Sch Med Harvard Partners Ctr Genet & Genom Boston MA 02115 USA Harvard Univ Sch Med Harvard MIT Div Hlth Sci & Technol Childrens Hosp Informat Program Boston MA 02115 USA 

出 版 物：《JOURNAL OF BIOMEDICAL INFORMATICS》 (生物医学情报学杂志)

年 卷 期：2009年第42卷第2期

页      面：287-295页

核心收录：

学科分类：1001[医学-基础医学(可授医学、理学学位)] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：V. Kann Rasmussen Foundation National Eye Institute National Institute of Diabetes and Digestive and Kidney Diseases National Cancer Institute National Institute of Allergy and Infectious Diseases Harvard-MIT Division of Health Sciences and Technology Lawson Wilkins Pediatric Endocrine Society 

主　　题：Bioinformatics Bayesian network Microarray RT-PCR Microarray data 

摘      要：Though genome-wide technologies, such as microarrays, are widely used, data from these methods are considered noisy: there is still varied Success in downstream biological validation. We report a method that increases the likelihood Of Successfully validating microarray findings using real time RT-PCR. including genes at low expression levels and with small differences. We use a Bayesian network to identify the most relevant sources of noise based on the successes and failures in validation for an initial set of selected genes, and then improve Our subsequent selection of genes for validation based on eliminating these sources of noise. The network displays the significant Sources of noise in an experiment, and scores the likelihood of validation for every gene. We show how the method can significantly increase validation success rates. In conclusion, in this study, we have Successfully added a new automated step to determine the contributory sources of noise that determine successful or unsuccessful downstream biological validation. (C) 2008 Elsevier Inc. All rights reserved.