Centre for High-Throughput Biology and Department of Psychiatry, 177 Michael Smith Laboratories, 2185 East Mall, University of British Columbia, Vancouver, BC V6T1Z4, Canada.
Bioinformatics. 2011 Jul 1;27(13):1860-6. doi: 10.1093/bioinformatics/btr288. Epub 2011 May 6.
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.
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.
Data and algorithm implementation are fully described and available at http://www.chibi.ubc.ca/extended. Programs are provided in Matlab m-code.
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。
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。
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。
