Inference of Regulatory Gene Interactions from Expression Data Using Three-Way Mutual Information
Inference of Regulatory Gene Interactions from Expression Data Using Three-Way Mutual Information
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DOI：
10.1111/j.1749-6632.2008.03757.x
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发表时间：
2009-01-01
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影响因子：
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通讯作者：
Anastassiou, Dimitris
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文献类型：
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作者：
Watkinson, John;Liang, Kuo-ching;Anastassiou, Dimitris
本文描述了在第二届逆向工程评估与方法对话会议（DREAM2）挑战5（盲法微阵列数据的无签名基因组规模网络预测）中指定的最佳表现技术。现有的算法使用基因表达水平的两两相关，这为调控相互作用的推断提供了有价值但不足的信息。在这里，我们提出了一种基于最近开发的相关上下文可能性（CLR）算法的计算方法，使用协同作用的信息论度量提取额外的互补信息，并为每个有序的基因对分配分数，测量第一个基因调节第二个基因的置信度。当在一组已知假设基础真理的公开可用大肠杆菌基因表达数据上进行测试时，与CLR相比，协同增强CLR （SA-CLR）算法具有显着提高的预测性能。由于确定了参与相互作用的最有可能的协同伙伴基因，也增强了生物学发现的潜力。
This paper describes the technique designated best performer in the 2nd conference on Dialogue for Reverse Engineering Assessments and Methods (DREAM2) Challenge 5 (unsigned genome-scale network prediction from blinded microarray data). Existing algorithms use the pairwise correlations of the expression levels of genes, which provide valuable but insufficient information for the inference of regulatory interactions. Here we present a computational approach based on the recently developed context likelihood of related (CLR) algorithm, extracting additional complementary information using the information theoretic measure of synergy and assigning a score to each ordered pair of genes measuring the degree of confidence that the first gene regulates the second. When tested on a set of publicly available Escherichia coli gene-expression data with known assumed ground truth, the synergy augmented CLR (SA-CLR) algorithm had significantly improved prediction performance when compared to CLR. There is also enhanced potential for biological discovery as a result of the identification of the most likely synergistic partner genes involved in the interactions.