用于综合基因表达分析和预测医学的自动化贝叶斯框架。
An automated bayesian framework for integrative gene expression analysis and predictive medicine.
作者信息
Parikh Neena, Zollanvari Amin, Alterovitz Gil
机构信息
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA;
出版信息
AMIA Jt Summits Transl Sci Proc. 2012;2012:95-104. Epub 2012 Mar 19.
MOTIVATION
This work constructs a closed loop Bayesian Network framework for predictive medicine via integrative analysis of publicly available gene expression findings pertaining to various diseases.
RESULTS
An automated pipeline was successfully constructed. Integrative models were made based on gene expression data obtained from GEO experiments relating to four different diseases using Bayesian statistical methods. Many of these models demonstrated a high level of accuracy and predictive ability. The approach described in this paper can be applied to any complex disorder and can include any number and type of genome-scale studies.
动机
本研究通过对与各种疾病相关的公开可用基因表达结果进行综合分析，构建了一个用于预测医学的闭环贝叶斯网络框架。
结果
成功构建了一个自动化流程。使用贝叶斯统计方法，基于从与四种不同疾病相关的基因表达 omnibus 实验（GEO）中获得的数据建立了综合模型。这些模型中的许多都表现出了很高的准确性和预测能力。本文所述方法可应用于任何复杂疾病，并且可以包括任意数量和类型的基因组规模研究。
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