Modularized learning of genetic interaction networks from biological annotations and mRNA expression data
Modularized learning of genetic interaction networks from biological annotations and mRNA expression data
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DOI：
10.1093/bioinformatics/bti406
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发表时间：
2005-06-01
期刊：
影响因子：
5.8
通讯作者：
Lee, D
中科院分区：
文献类型：
--
作者：
Lee, PH;Lee, D
动机：使用贝叶斯网络推断遗传相互作用机制，由于其良好的理论基础和统计稳健性，最近引起了越来越多的关注。结果：我们提出了一种新的基于先验知识的遗传网络推断方法，解决了现有基因表达数据不足的问题。我们将提出的方法称为模块化网络学习(MONET)。该方法首先将一个完整的基因集合划分为重叠的模块，同时考虑生物标注和表达数据。其次，它为每个模块推断一个贝叶斯网络，并将学习到的子网络集成到一个全局网络中。提出了一种基于生物注释的层次性、特异性和多样性来度量基因间相似性的算法。所提出的方法描绘了模块间关系的全局图景以及模块内交互的详细外观。我们应用所提出的方法分析了酿酒酵母的胁迫数据，发现了几个假设来推测未分类基因的假定功能。我们还将该方法与一种基于集合的方法和两种基于表达式的聚类方法进行了比较。
Motivation: Inferring the genetic interaction mechanism using Bayesian networks has recently drawn increasing attention due to its well-established theoretical foundation and statistical robustness. However, the relative insufficiency of experiments with respect to the number of genes leads to many false positive inferences.Results: We propose a novel method to infer genetic networks by alleviating the shortage of available mRNA expression data with prior knowledge. We call the proposed method 'modularized network learning' (MONET). Firstly, the proposed method divides a whole gene set to overlapped modules considering biological annotations and expression data together. Secondly, it infers a Bayesian network for each module, and integrates the learned subnetworks to a global network. An algorithm that measures a similarity between genes based on hierarchy, specificity and multiplicity of biological annotations is presented. The proposed method draws a global picture of inter-module relationships as well as a detailed look of intra-module interactions. We applied the proposed method to analyze Saccharomyces cerevisiae stress data, and found several hypotheses to suggest putative functions of unclassified genes. We also compared the proposed method with a whole-set-based approach and two expression-based clustering approaches.