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DATE Analysis: A General Theory of Biological Change Applied to Microarray Data

机译:日期分析:生物变化的通用理论应用于微阵列数据

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In contrast to conventional data mining, which searches for specific subsets of genes (extensive variables) to correlate with specific phenotypes, DATE analysis correlates intensive state variables calculated from the same datasets. At the heart of DATE analysis are two biological equations of state not dependent on genetic pathways. This result distinguishes DATE analysis from other bioinformatics approaches. The dimensionless state variable F quantifies the relative overall cellular activity of test cells compared to well-chosen reference cells. The variable π_i is the fold-change in the expression of the ith gene of test cells relative to reference. It is the fraction φ of the genome undergoing differential expression-not the magnitude n-that controls biological change. The state variable φ is equivalent to the control strength of metabolic control analysis. For tractability, DATE analysis assumes a linear system of enzyme-connected networks and exploits the small average contribution of each cellular component. This approach was validated by reproducible values of the state variables F, RNA index, and φ calculated from random subsets of transcript micro-array data. Using published microarray data, F, RNA index, and φ were correlated with: (1) the blood-feeding cycle of the malaria parasite, (2) embryonic development of the fruit fly, (3) temperature adaptation of Killifish, (4) exponential growth of cultured S. pneumoniae, and (5) human cancers. DATE analysis was applied to aCGH data from the great apes. A good example of the power of DATE analysis is its application to genomically unstable cancers, which have been refractory to data mining strategies.
机译:与传统的数据挖掘相反,传统的数据挖掘通过搜索特定的基因子集(广泛的变量)来与特定的表型相关联,而DATE分析则将根据相同数据集计算出的密集状态变量进行关联。 DATE分析的核心是两个不依赖遗传途径的生物学状态方程。该结果将DATE分析与其他生物信息学方法区分开来。与选择好的参考细胞相比,无量纲状态变量F量化了测试细胞的相对总体细胞活性。变量π_i是测试细胞的第i个基因相对于参考的表达的倍数变化。控制差异的是基因组经历差异表达的分数φ而非大小n。状态变量等于代谢控制分析的控制强度。为了便于处理,DATE分析假设酶连接网络为线性系统,并利用每个细胞成分的较小平均贡献。通过从转录本微阵列数据的随机子集计算出的状态变量F,RNA指数和φ的可复制值,验证了该方法。使用公开的微阵列数据,F,RNA指数和φ与以下各项相关:(1)疟原虫的血液喂养周期;(2)果蝇的胚胎发育;(3)of鱼的温度适应性;(4)培养的肺炎链球菌的指数增长,以及(5)人类癌症。 DATE分析应用于大猿的aCGH数据。 DATE分析的强大功能的一个很好的例子是它在基因组不稳定的癌症中的应用,这种癌症对于数据挖掘策略是难以接受的。

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