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Inference of Gene Regulatory Networks Using Time Sliding Comparison and Transcriptional Lagging Time from Time Series Gene Expression Profiles

机译:使用时间滑动比较和从时间序列基因表达轮廓的转录滞后时间推断基因调节网络

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Inference of gene regulatory network from microarray data is one of the most important issues in bioinformatics. Several algorithms have been introduced for this problem, but they cannot give accurate results in case of time series gene expression data. Here, we propose an algorithm that predicts the gene regulatory network more accurately than the previous methods. A new method finds the relationship between a pair of genes by time-shifting the time series data of one gene against another when we compare the patterns of the gene pair. In addition, we increase the accuracy of the prediction method by filtering out the interactions that cannot exist in real biological network. We tested the algorithm to several simulated data which are based on realistic enzyme kinetics system, and evaluated the effectiveness of our algorithm. Results show that the present algorithm significantly improves the accuracy of the inference of gene regulatory network.
机译:来自微阵列数据的基因监管网络推断是生物信息学中最重要的问题之一。已经介绍了几种算法的这个问题,但在时间序列基因表达数据的情况下,它们不能给出准确的结果。在这里,我们提出了一种算法,其比以前的方法更准确地预测基因调节网络。当我们比较基因对的图案时,通过将一个基因的时间序列数据逐时地将一个基因的时间序列数据逐时地发现了一对基因之间的关系。此外,我们通过滤除真实生物网络中不存在的交互来提高预测方法的准确性。我们将该算法测试到几种基于现实酶动力学系统的模拟数据,并评估了算法的有效性。结果表明,本算法显着提高了基因监管网络推断的准确性。

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