MOLECULAR NETWORKS IN MICROARRAY ANALYSIS
MOLECULAR NETWORKS IN MICROARRAY ANALYSIS
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DOI：
10.1142/s0219720007002795
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发表时间：
2007-04-01
影响因子：
1
通讯作者：
Mazo, Ilya
中科院分区：
文献类型：
--
作者：
Sivachenko, Andrey Y.;Yuryev, Anton;Mazo, Ilya
基于微阵列的组织、细胞和疾病状态以及环境条件和治疗反应的表征提供了包含大量宝贵信息的全基因组快照。然而，数据中缺乏内在结构和强噪声使得提取和解释这些信息以及制定和优先考虑领域相关假设成为困难的任务。需要与不同类型的生物数据集成，以将表达测量置于具有生物学意义的背景中。讨论了微阵列数据解释的几种方法，重点是分子网络信息的使用。统计程序被证明将表达数据叠加到从科学文献中挖掘的转录调控网络上，旨在选择下游具有显着表达变化模式的转录调控子。建议进行测试，考虑网络拓扑和转录调控效应的迹象。使用两个不同的表达数据集说明了这些方法，比较了性能，并讨论了预测的生物学相关性。
Microarray-based characterization of tissues, cellular and disease states, and environmental condition and treatment responses provides genome-wide snapshots containing large amounts of invaluable information. However, the lack of inherent structure within the data and strong noise make extracting and interpreting this information and formulating and prioritizing domain relevant hypotheses difficult tasks. Integration with different types of biological data is required to place the expression measurements into a biologically meaningful context. A few approaches in microarray data interpretation are discussed with the emphasis on the use of molecular network information. Statistical procedures are demonstrated that superimpose expression data onto the transcription regulation network mined from scientific literature and aim at selecting transcription regulators with significant patterns of expression changes downstream. Tests are suggested that take into account network topology and signs of transcription regulation effects. The approaches are illustrated using two different expression datasets, the performance is compared, and biological relevance of the predictions is discussed.