
MPGM: Scalable and Accurate Multiple Network Alignment
Proteinprotein interaction (PPI) network alignment is a canonical opera...
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Projected Power Iteration for Network Alignment
The network alignment problem asks for the best correspondence between t...
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A comprehensive statistical study of metabolic and proteinprotein interaction network properties
Understanding the mathematical properties of graphs underling biological...
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Low rank methods for multiple network alignment
Multiple network alignment is the problem of identifying similar and rel...
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On function homophily of microbial ProteinProtein Interaction Networks
We present a new method for assessing homophily in networks whose vertic...
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GoTWAVE: Temporal network alignment using graphletorbit transitions
Global pairwise network alignment (GPNA) aims to find a onetoone node ...
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Exact, Parallelizable Dynamic Time Warping Alignment with Linear Memory
Audio alignment is a fundamental preprocessing step in many MIR pipeline...
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Exact pvalues for global network alignments via combinatorial analysis of shared GO terms
Network alignment aims to uncover topologically similar regions in the proteinprotein interaction (PPI) networks of two or more species under the assumption that topologically similar regions perform similar functions. Although there exist a plethora of both network alignment algorithms and measures of topological similarity, currently no "gold standard" exists for evaluating how well either is able to uncover functionally similar regions. Here we propose a formal, mathematically and statistically rigorous method for evaluating the statistical significance of shared GO terms in a global, 1to1 alignment between two PPI networks. We use combinatorics to precisely count the number of possible network alignments in which k proteins share a particular GO term. When divided by the number of all possible network alignments, this provides an explicit, exact pvalue for a network alignment with respect to a particular GO term.
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