Corbi: a new R package for biological network alignment and querying.
Corbi: a new R package for biological network alignment and querying.
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Corbi：用于生物网络对齐和查询的新 R 包
DOI：
10.1186/1752-0509-7-s2-s6
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发表时间：
2013
影响因子：
--
通讯作者：
Zhang XS
中科院分区：
文献类型：
--
作者：
Huang Q;Wu LY;Zhang XS
在过去的十年里，大量的生物网络是建立在快速发展的高通量技术产生的大规模实验数据以及文献和其他来源。但是由于生物网络分析工具的有限性，大量的网络数据没有得到充分的利用。生物网络比对和查询作为一种基本的生物信息学方法，在预测新的蛋白质相互作用（PPI）等领域得到了广泛的应用。虽然已经发表了许多算法，但网络对齐和查询问题并没有得到令人满意的解决。在本文中，我们扩展CNetQ，一种新的网络查询方法的基础上的条件随机场模型，解决网络对齐问题，通过采用迭代双向映射策略。在50个模拟和3个真实的PPI网络比对实例上，采用4种结构和5种生物学指标，将CNetA方法与其他4种方法进行了比较。对生物网络进化模型生成的模拟数据进行计算实验，验证了网络对齐方法的有效性，结果表明，CNetA算法在节点和网络两方面都获得了最好的准确性。对于真实的数据，CNetA能识别出较大的生物保守子网络和较大的连通子网络，分别与结构主导和生物主导的方法进行比较，表明CNetA能更好地平衡生物和结构的相似性.此外，CNetQ和CNetA已在新的R包Corbi（ http://doc.aporc.org/wiki/Corbi ），并基于R软件包构建了CNetQ和CNetA的Web服务。本文中使用的模拟数据集和真实的数据集可从以下网址下载： http://doc.aporc.org/wiki/CNetA/
In the last decade, plenty of biological networks are built from the large scale experimental data produced by the rapidly developing high-throughput techniques as well as literature and other sources. But the huge amount of network data have not been fully utilized due to the limited biological network analysis tools. As a basic and essential bioinformatics method, biological network alignment and querying have been applied in many fields such as predicting new protein-protein interactions (PPI). Although many algorithms were published, the network alignment and querying problems are not solved satisfactorily. In this paper, we extended CNetQ, a novel network querying method based on the conditional random fields model, to solve network alignment problem, by adopting an iterative bi-directional mapping strategy. The new method, called CNetA, was compared with other four methods on fifty simulated and three real PPI network alignment instances by using four structural and five biological measures. The computational experiments on the simulated data, which were generated from a biological network evolutionary model to validate the effectiveness of network alignment methods, show that CNetA gets the best accuracy in terms of both nodes and networks. For the real data, larger biological conserved subnetworks and larger connected subnetworks were identified, compared with the structural-dominated methods and the biological-dominated methods, respectively, which suggests that CNetA can better balances the biological and structural similarities. Further, CNetQ and CNetA have been implemented in a new R package Corbi ( http://doc.aporc.org/wiki/Corbi ), and freely accessible and easy used web services for CNetQ and CNetA have also been constructed based on the R package. The simulated and real datasets used in this paper are available for downloading at http://doc.aporc.org/wiki/CNetA/