从基因表达数据中挖掘延迟基因调控模式

Huang-Cheng Kuo, Pei-Cheng Tsai
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引用次数: 2

摘要

已发现的基因调控网络对预测未知的基因功能非常有帮助。基因和基因之间的激活和失活关系是从微阵列基因表达数据中挖掘出来的。有证据表明,基因调控过程中存在多个时间单位的延迟。关联规则挖掘技术非常适用于发现基因间的调节关系。然而,当前的关联规则挖掘技术不能处理临时排序的事务。我们提出了一种改进的关联规则挖掘技术来有效地发现基因间的时滞调节关系。通过分析基因表达数据,我们可以发现基因之间的关系。因此,我们使用改进的关联规则来挖掘基因调控模式。我们提出的BC3方法旨在从时间序列基因表达数据中挖掘长度为3的时间延迟基因调控模式。但是前面两项是监管机构,最后一项是他们的影响对象。首先,我们使用Apriori来查找频繁的2项集,以便向后推算到BL1。Apriori挖掘了同一时间点的频繁2项集,因此我们将L2分割为长度为1的L2,因为它们在同一时间点上有关系。然后我们将BL1和L1组合成一个新的具有时滞关系的有序集BC2。用阈值对BC2进行剪枝后,得到BL2。结果由BL2与BC3结合,从BC3中筛选出BL3得到。我们用酵母基因表达数据对我们的方法进行了评价,并对结果进行了分析,证明了我们的工作是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mining Time-delayed Gene Regulation Patterns from Gene Expression Data
Discovered gene regulation networks are very helpful to predict unknown gene functions. The activating and deactivating relations between genes and genes are mined from microarray gene expression data. There are evidences showing that multiple time units delay exist in a gene regulation process. Association rule mining technique is very suitable for finding regulation relations among genes. However, current association rule mining techniques cannot handle temporally ordered transactions. We propose a modified association rule mining technique for efficiently discovering time-delayed regulation relationships among genes. By analyzing gene expression data, we can discover gene relations. Thus, we use modified association rule to mine gene regulation patterns. Our proposed method, BC3, is designed to mine time-delayed gene regulation patterns with length 3 from time series gene expression data. However, the front two items are regulators, and the last item is their affecting target. First we use Apriori to find frequent 2-itemset in order to figure backward to BL1. The Apriori mined the frequent 2-itemset in the same time point, so we make the L2 split to length one for having relation in the same time point. Then we combine BL1 with L1 to a new ordered-set BC2 with time-delayed relations. After pruning BC2 with the threshold, BL2 is derived. The results are worked out by BL2 joining itself to BC3, and sifting BL3 from BC3. We use yeast gene expression data to evaluate our method and analyze the results to show our work is efficient.
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