基因网络中间接连接在预测功能中的作用。
The role of indirect connections in gene networks in predicting function.
机构信息
Centre for High-Throughput Biology and Department of Psychiatry, 177 Michael Smith Laboratories, 2185 East Mall, University of British Columbia, Vancouver, BC V6T1Z4, Canada.
出版信息
Bioinformatics. 2011 Jul 1;27(13):1860-6. doi: 10.1093/bioinformatics/btr288. Epub 2011 May 6.
Abstract
MOTIVATION
Gene networks have been used widely in gene function prediction algorithms, many based on complex extensions of the 'guilt by association' principle. We sought to provide a unified explanation for the performance of gene function prediction algorithms in exploiting network structure and thereby simplify future analysis.
RESULTS
We use co-expression networks to show that most exploited network structure simply reconstructs the original correlation matrices from which the co-expression network was obtained. We show the same principle works in predicting gene function in protein interaction networks and that these methods perform comparably to much more sophisticated gene function prediction algorithms.
AVAILABILITY AND IMPLEMENTATION
Data and algorithm implementation are fully described and available at http://www.chibi.ubc.ca/extended. Programs are provided in Matlab m-code.
CONTACT
摘要
动机
基因网络已被广泛应用于基因功能预测算法中，其中许多算法都是基于“关联即有罪”原则的复杂扩展。我们试图为基因功能预测算法利用网络结构的性能提供一个统一的解释，从而简化未来的分析。
结果
我们使用共表达网络表明，大多数被利用的网络结构只是从获得共表达网络的原始相关矩阵中重建而来。我们展示了相同的原理在预测蛋白质相互作用网络中的基因功能方面同样适用，并且这些方法的性能与更为复杂的基因功能预测算法相当。
可用性和实现
数据和算法实现都有详细的描述，并可在 http://www.chibi.ubc.ca/extended 上获得。程序以 Matlab m 代码的形式提供。
联系方式
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本文引用的文献
1
The impact of multifunctional genes on "guilt by association" analysis.多功能基因对“关联定罪”分析的影响。
PLoS One. 2011 Feb 18;6(2):e17258. doi: 10.1371/journal.pone.0017258.
2
The genetic landscape of a cell.细胞的基因图谱。
Science. 2010 Jan 22;327(5964):425-31. doi: 10.1126/science.1180823.
3
Predicting gene function using hierarchical multi-label decision tree ensembles.基于层次多标签决策树集成模型预测基因功能。
BMC Bioinformatics. 2010 Jan 2;11:2. doi: 10.1186/1471-2105-11-2.
4
MINT, the molecular interaction database: 2009 update.MINT，分子相互作用数据库：2009 年更新。
Nucleic Acids Res. 2010 Jan;38(Database issue):D532-9. doi: 10.1093/nar/gkp983. Epub 2009 Nov 6.
5
Human Protein Reference Database and Human Proteinpedia as discovery tools for systems biology.人类蛋白质参考数据库和人类蛋白质百科全书作为系统生物学的发现工具。
Methods Mol Biol. 2009;577:67-79. doi: 10.1007/978-1-60761-232-2_6.
6
High-throughput, quantitative analyses of genetic interactions in E. coli.大肠杆菌中基因相互作用的高通量定量分析。
Nat Methods. 2008 Sep;5(9):781-7. doi: 10.1038/nmeth.1240.
7
iRefIndex: a consolidated protein interaction database with provenance.iRefIndex：一个具有来源信息的整合蛋白质相互作用数据库。
BMC Bioinformatics. 2008 Sep 30;9:405. doi: 10.1186/1471-2105-9-405.
8
InnateDB: facilitating systems-level analyses of the mammalian innate immune response.InnateDB：助力哺乳动物先天免疫反应的系统级分析。
Mol Syst Biol. 2008;4:218. doi: 10.1038/msb.2008.55. Epub 2008 Sep 2.
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