Using Bayesian networks to analyze expression data
Using Bayesian networks to analyze expression data
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DOI：
10.1089/106652700750050961
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发表时间：
2000-01-01
影响因子：
1.7
通讯作者：
Pe'er, D
中科院分区：
文献类型：
--
作者：
Friedman, N;Linial, M;Pe'er, D
DNA 杂交阵列可同时测量数千个基因的表达水平。这些测量提供了细胞内转录水平的“快照”。计算生物学的一个主要挑战是通过此类测量揭示基因/蛋白质相互作用和细胞系统的关键生物学特征。在本文中，我们提出了一个新的框架，用于基于多种表达测量来发现基因之间的相互作用。该框架建立在使用贝叶斯网络来表示统计依赖性的基础上。贝叶斯网络是基于图的联合多元概率分布模型，可捕获变量之间条件独立性的属性。这些模型之所以有吸引力，是因为它们能够描述复杂的随机过程，并且因为它们提供了从（嘈杂的）观察中学习的清晰方法。我们首先展示贝叶斯网络如何描述基因之间的相互作用，然后描述一种使用贝叶斯网络学习工具从微阵列数据中恢复基因相互作用的方法。最后，我们在 Spellman 等人 (1998) 的酿酒酵母细胞周期测量中证明了这种方法。
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of transcription levels within the cell. A major challenge in computational biology is to uncover, from such measurements, gene/protein interactions and key biological features of cellular systems. In this paper, we propose a new framework for discovering interactions between genes based on multiple expression measurements. This framework builds on the use of Bayesian networks for representing statistical dependencies. A Bayesian network is a graph-based model of joint multivariate probability distributions that captures properties of conditional independence between variables. Such models are attractive for their ability to describe complex stochastic processes and because they provide a clear methodology for learning from (noisy) observations. We start by showing how Bayesian networks can describe interactions between genes, We then describe a method for recovering gene interactions from microarray data using tools for learning Bayesian networks. Finally, we demonstrate this method on the S. cerevisiae cell-cycle measurements of Spellman et al, (1998).