Mutual information estimation reveals global associations between stimuli and biological processes.
Mutual information estimation reveals global associations between stimuli and biological processes.
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DOI：
10.1186/1471-2105-10-s1-s52
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发表时间：
2009-01-30
影响因子：
3
通讯作者：
Sese J
中科院分区：
文献类型：
--
作者：
Suzuki T;Sugiyama M;Kanamori T;Sese J
虽然微阵列基因表达分析已变得流行，但仍然难以解释由刺激或条件变化引起的生物学变化。将基因聚类并将每个组与生物学功能相关联是常用的方法。然而，这种方法只能检测细胞过程中的部分变化。在此，我们提出了一种方法，通过将观察到的基因表达条件与基因功能相关联来发现细胞内的全局变化。为了阐明这种关联，我们引入了一种新的特征选择方法，称为最小二乘互信息（LSMI），它计算互信息而不需要密度估计，因此LSMI可以检测细胞内的非线性关联。通过与现有方法的比较，我们证明了LSMI的有效性。应用于酵母微阵列数据集的结果表明，非自然刺激影响各种生物过程，而其他人没有显着关系到特定的细胞过程。此外，我们发现，生物过程可以分为四种类型，根据不同的刺激的反应：DNA/RNA代谢，基因表达，蛋白质代谢和蛋白质定位。提出了一种新的特征选择方法LSMI，并将其应用于微阵列数据集中酵母菌状态与生物过程之间的关联挖掘。事实上，LSMI使我们能够阐明细胞过程控制的全局组织。
Although microarray gene expression analysis has become popular, it remains difficult to interpret the biological changes caused by stimuli or variation of conditions. Clustering of genes and associating each group with biological functions are often used methods. However, such methods only detect partial changes within cell processes. Herein, we propose a method for discovering global changes within a cell by associating observed conditions of gene expression with gene functions. To elucidate the association, we introduce a novel feature selection method called Least-Squares Mutual Information (LSMI), which computes mutual information without density estimaion, and therefore LSMI can detect nonlinear associations within a cell. We demonstrate the effectiveness of LSMI through comparison with existing methods. The results of the application to yeast microarray datasets reveal that non-natural stimuli affect various biological processes, whereas others are no significant relation to specific cell processes. Furthermore, we discover that biological processes can be categorized into four types according to the responses of various stimuli: DNA/RNA metabolism, gene expression, protein metabolism, and protein localization. We proposed a novel feature selection method called LSMI, and applied LSMI to mining the association between conditions of yeast and biological processes through microarray datasets. In fact, LSMI allows us to elucidate the global organization of cellular process control.