通过multiMAGNA ++实现多网络对齐
Multiple network alignment via multiMAGNA++
摘要：
网络比对（NA）的目的是找到不同物种的分子网络之间的节点映射，以识别拓扑或功能相似的网络区域。类似于基因组序列比对，NA可用于在比对的网络区域之间将生物学知识从研究良好的物种转移到研究较差的物种。成对NA（PNA）发现两个网络之间的相似区域，而多NA（MNA）可以对齐两个以上的网络。我们专注于MNA。现有的MNA方法旨在最大限度地提高所有对齐节点的总相似性（节点保护）。然后，他们通过测量保守边缘的数量来评估对齐质量，但只有在构建对齐之后。除了节点保持之外，在比对构建期间直接优化边缘保持可以导致更优的比对。因此，我们提出了一种新的MNA方法，称为multiMAGNA++，可以实现这一点。事实上，multiMAGNA++通常优于或与现有的MNA方法相当，同时通常比现有的方法完成得更快。也就是说，multiMAGNA++可以很好地扩展到更大的网络数据，并且可以有效地并行化。在方法评估过程中，我们还引入了新的MNA质量措施，以允许更完整的对齐特性以及更公平的MNA方法比较相比，只使用现有的对齐质量措施。
英文摘要：
Network alignment (NA) aims to find a node mapping between molecular networks of different species that identifies topologically or functionally similar network regions. Analogous to genomic sequence alignment, NA can be used to transfer biological knowledge from well- to poorly-studied species between aligned network regions. Pairwise NA (PNA) finds similar regions between two networks while multiple NA (MNA) can align more than two networks. We focus on MNA. Existing MNA methods aim to maximize total similarity over all aligned nodes (node conservation). Then, they evaluate alignment quality by measuring the amount of conserved edges, but only after the alignment is constructed. Directly optimizing edge conservation during alignment construction in addition to node conservation may result in superior alignments. Thus, we present a novel MNA approach called multiMAGNA++ that can achieve this. Indeed, multiMAGNA++ generally outperforms or is on par with the existing MNA methods, while often completing faster than the existing methods. That is, multiMAGNA++ scales well to larger network data and can be parallelized effectively. During method evaluation, we also introduce new MNA quality measures to allow for more complete alignment characterization as well as more fair MNA method comparison compared to using only the existing alignment quality measures.
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