Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, United States of America.
PLoS One. 2012;7(8):e41474. doi: 10.1371/journal.pone.0041474. Epub 2012 Aug 13.
In this work, we introduce a novel network synthesis model that can generate families of evolutionarily related synthetic protein-protein interaction (PPI) networks. Given an ancestral network, the proposed model generates the network family according to a hypothetical phylogenetic tree, where the descendant networks are obtained through duplication and divergence of their ancestors, followed by network growth using network evolution models. We demonstrate that this network synthesis model can effectively create synthetic networks whose internal and cross-network properties closely resemble those of real PPI networks. The proposed model can serve as an effective framework for generating comprehensive benchmark datasets that can be used for reliable performance assessment of comparative network analysis algorithms. Using this model, we constructed a large-scale network alignment benchmark, called NAPAbench, and evaluated the performance of several representative network alignment algorithms. Our analysis clearly shows the relative performance of the leading network algorithms, with their respective advantages and disadvantages. The algorithm and source code of the network synthesis model and the network alignment benchmark NAPAbench are publicly available at http://www.ece.tamu.edu/bjyoon/NAPAbench/.
在这项工作中，我们引入了一种新的网络综合模型，该模型可以生成具有进化关系的合成蛋白质-蛋白质相互作用（PPI）网络家族。给定一个祖先网络，所提出的模型根据假设的系统发育树生成网络家族，其中后代网络通过祖先的复制和分歧得到，然后使用网络进化模型进行网络生长。我们证明，该网络综合模型可以有效地创建合成网络，其内部和跨网络特性与真实 PPI 网络非常相似。该模型可以作为一个有效的框架，用于生成全面的基准数据集，可用于比较网络分析算法的可靠性能评估。使用该模型，我们构建了一个大型网络对齐基准，称为 NAPAbench，并评估了几种代表性的网络对齐算法的性能。我们的分析清楚地显示了领先网络算法的相对性能，以及它们各自的优缺点。网络综合模型和网络对齐基准 NAPAbench 的算法和源代码可在 http://www.ece.tamu.edu/bjyoon/NAPAbench/ 上获得。
