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使用MetaCore和MetaDrug平台进行系统ADME/Tox网络分析的算法

Algorithms for network analysis in systems-ADME/Tox using the MetaCore and MetaDrug platforms.

作者信息

Ekins S, Bugrim A, Brovold L, Kirillov E, Nikolsky Y, Rakhmatulin E, Sorokina S, Ryabov A, Serebryiskaya T, Melnikov A, Metz J, Nikolskaya T

机构信息

GeneGo Inc, St Joseph, MI, USA.

出版信息

Xenobiotica. 2006 Oct-Nov;36(10-11):877-901. doi: 10.1080/00498250600861660.

Abstract

The authors have previously applied two integrated platforms, MetaCore and MetaDrug, for the assembly and analysis of human biological networks as a useful method for the integration and functional interpretation of high-throughput experimental data. The present study demonstrates in detail the specific algorithms that are used in both software platforms. Using a standard set of genes as input, namely CYP3A4 (an enzyme), PXR (a nuclear hormone receptor), MDR1 (a transporter) and hERG (an ion channel) related to the absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) of xenobiotics, we have now generated networks with each algorithm. The relative advantages and disadvantages of these algorithms are explained using these examples as well as appropriate instances of utility to illustrate further the particular circumstances for their use. In addition, the benefits of the different network algorithms are identified when compared with algorithms available in other products, where this information is available.

摘要

作者之前应用了两个综合平台,即MetaCore和MetaDrug,用于构建和分析人类生物网络,作为整合和功能解读高通量实验数据的一种有用方法。本研究详细展示了这两个软件平台所使用的具体算法。以一组标准基因为输入,即与异生物素的吸收、分布、代谢、排泄和毒性(ADME/Tox)相关的CYP3A4(一种酶)、PXR(一种核激素受体)、MDR1(一种转运蛋白)和hERG(一种离子通道),我们现在用每种算法生成了网络。利用这些例子以及适当的实用实例来解释这些算法的相对优缺点,以进一步说明其使用的具体情况。此外,在有其他产品算法信息可资比较时,还确定了不同网络算法的优势。

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