Dynamic modeling of gene expression data
Dynamic modeling of gene expression data
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DOI：
10.1073/pnas.98.4.1693
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发表时间：
2001-02-13
影响因子：
11.1
通讯作者：
Banavar, JR
中科院分区：
文献类型：
--
作者：
Holter, NS;Maritan, A;Banavar, JR
我们通过时间翻译矩阵来描述基因表达水平的时间演化，并基于基因在某个初始时间的表达水平来预测基因未来的表达水平。我们通过使用奇异值分解获得的特征模式在线性框架内建模，推导出先前发表的DNA微阵列基因表达数据集的时间平移矩阵。由此产生的时间转换矩阵提供了模式之间关系的度量，并控制了它们的时间演化。我们证明了仅连接几个模态的截断矩阵是全时间平移矩阵的良好近似值。这一发现表明，基因之间的基本连接数量很少。
We describe the time evolution of gene expression levels by using a time translational matrix to predict future expression levels of genes based on their expression levels at some initial time. We deduce the time translational matrix for previously published DNA microarray gene expression data sets by modeling them within a linear framework by using the characteristic modes obtained by singular value decomposition. The resulting time translation matrix provides a measure of the relationships among the modes and governs their time evolution. We show that a truncated matrix linking just a few modes is a good approximation of the full time translation matrix. This finding suggests that the number of essential connections among the genes is small.