SANA：生物网络比对中将搜索算法与目标函数分离，第1部分：搜索

SANA：模拟退火网络对齐在生物网络中的应用

SANA: Simulated Annealing Network Alignment Applied to Biological Networks

生物网络的比对有可能像序列比对一样教会我们更多关于生物学和疾病的知识。序列比对可以在多项式时间内得到最优解。相比之下，网络比对是$NP$ -hard，这意味着不可能找到最佳解决方案，并且找到的比对的质量强烈依赖于用于创建它们的算法。每个网络对齐算法由两个正交分量组成：第一，目标函数或度量$M$，用于评估任何建议的对齐的质量;第二，搜索算法，用于探索指数级大的可能对齐集合，以根据$M$找到“好”的对齐。目标函数分为许多类别，包括生物学度量，如序列相似性，以及拓扑度量，如图相似性和边缘覆盖（可能加权）。搜索所有可能的对齐空间的算法可以是确定性的或随机的，并且在过去十年中已经提供了许多可能性。本文介绍了一种新的随机搜索算法SANA：Simulated Annealing Network Aligner。我们测试了几个流行的目标函数，并证明它几乎普遍优化每一个显着优于现有的搜索算法。最后，我们比较了几个拓扑目标函数使用SANA。软件可在http://sana.ics.uci.edu上获得。

An introductory guide to aligning networks using SANA, the Simulated Annealing Network Aligner

Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological {\em networks} holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology -- the "structure" of the network -- is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment -- which is an essentially solved problem -- network c…

SANA NetGO：使用基因本体（GO）术语对网络比对进行评分的组合方法

SANA NetGO: A combinatorial approach to using Gene Ontology (GO) terms to score network alignments

基因本体（GO）术语经常用于对蛋白质相互作用（PPI）网络之间的比对进行评分。存在测量两种蛋白质之间的GO相似性的方法，但是网络比对中的蛋白质对不是孤立的：每个配对通过比对本身隐含地依赖于每个其他配对。当前的方法未能考虑GO项跨网络的频率，并且试图通过基于GO项在GO层级中的位置对何时“允许”GO项施加任意规则，而不是使用PPI网络本身中容易获得的频率信息，来以自组织方式考虑常见的GO项。在这里，我们开发了一个新的措施，NetGO，自然权重不频繁，信息GO条款更重频繁，信息较少的GO条款，而不需要任意截止。具体而言，NetGO根据频繁GO术语在被对齐的网络中的频率来降低其得分。这是仅适用于比对的全局度量，独立于成对GO度量，在相同的意义上，基于边缘的EC或S3分数是拓扑相似性的全局度量，独立于成对拓扑相似性。我们证明了NetGO的优越性，通过创建基于同源对节点的预定质量的路线，并表明NetGO与路线质量的相关性远远优于任何现有的基于GO的路线措施。我们还证明，NetGO提供了一个物种之间的分类相似性的措施，与现有的分类措施-一个功能不共享与现有的GO为基础的网络对齐措施一致。最后，我们从以前的研究中重新评分了近十几个校准器产生的校准，并表明NetGO在区分好的校准和坏的校准方面比现有的措施做得更好。

The basis of easy controllability in Boolean networks

Effective control of biological systems can often be achieved through the control of a surprisingly small number of distinct variables. We bring clarity to such results using the formalism of Boolean dynamical networks, analyzing the effectiveness of external control in selecting a desired final state when that state is among the original attractors of the dynamics. Analyzing 49 existing biological network models, we find strong numerical evidence that the average number of nodes that must be forced scales logarithmically with the number of original attractors. This suggests that biological ne…

From homogeneous to heterogeneous network alignment via colored graphlets

Network alignment (NA) compares networks with the goal of finding a node mapping that uncovers highly similar (conserved) network regions. Existing NA methods are homogeneous, i.e., they can deal only with networks containing nodes and edges of one type. Due to increasing amounts of heterogeneous network data with nodes or edges of different types, we extend three recent state-of-the-art homogeneous NA methods, WAVE, MAGNA++, and SANA, to allow for heterogeneous NA for the first time. We introduce several algorithmic novelties. Namely, these existing methods compute homogeneous graphlet-based …

On the current failure -- but bright future -- of topology-driven biological network alignment

The function of a protein is defined by its interaction partners. Thus, topology-driven network alignment of the protein-protein interaction (PPI) networks of two species should uncover similar interaction patterns and allow identification of functionally similar proteins. Howver, few of the fifty or more algorithms for PPI network alignment have demonstrated a significant link between network topology and functional similarity, and none have recovered orthologs using network topology alone. We find that the major contributing factors to this failure are: (i) edge densities in current PPI netw…