14,449 research outputs found

    Domain-oriented edge-based alignment of protein interaction networks

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    Motivation: Recent advances in high-throughput experimental techniques have yielded a large amount of data on proteinā€“protein interactions (PPIs). Since these interactions can be organized into networks, and since separate PPI networks can be constructed for different species, a natural research direction is the comparative analysis of such networks across species in order to detect conserved functional modules. This is the task of network alignment

    A survey of statistical network models

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    Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference

    Fair Evaluation of Global Network Aligners

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    Biological network alignment identifies topologically and functionally conserved regions between networks of different species. It encompasses two algorithmic steps: node cost function (NCF), which measures similarities between nodes in different networks, and alignment strategy (AS), which uses these similarities to rapidly identify high-scoring alignments. Different methods use both different NCFs and different ASs. Thus, it is unclear whether the superiority of a method comes from its NCF, its AS, or both. We already showed on MI-GRAAL and IsoRankN that combining NCF of one method and AS of another method can lead to a new superior method. Here, we evaluate MI-GRAAL against newer GHOST to potentially further improve alignment quality. Also, we approach several important questions that have not been asked systematically thus far. First, we ask how much of the node similarity information in NCF should come from sequence data compared to topology data. Existing methods determine this more-less arbitrarily, which could affect the resulting alignment(s). Second, when topology is used in NCF, we ask how large the size of the neighborhoods of the compared nodes should be. Existing methods assume that larger neighborhood sizes are better. We find that MI-GRAAL's NCF is superior to GHOST's NCF, while the performance of the methods' ASs is data-dependent. Thus, the combination of MI-GRAAL's NCF and GHOST's AS could be a new superior method for certain data. Also, which amount of sequence information is used within NCF does not affect alignment quality, while the inclusion of topological information is crucial. Finally, larger neighborhood sizes are preferred, but often, it is the second largest size that is superior, and using this size would decrease computational complexity. Together, our results give several general recommendations for a fair evaluation of network alignment methods.Comment: 19 pages. 10 figures. Presented at the 2014 ISMB Conference, July 13-15, Boston, M

    Scalable global alignment for multiple biological networks

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    <p>Abstract</p> <p>Background</p> <p>Advances in high-throughput technology has led to an increased amount of available data on protein-protein interaction (PPI) data. Detecting and extracting functional modules that are common across multiple networks is an important step towards understanding the role of functional modules and how they have evolved across species. A global protein-protein interaction network alignment algorithm attempts to find such functional orthologs across multiple networks.</p> <p>Results</p> <p>In this article, we propose a scalable global network alignment algorithm based on clustering methods and graph matching techniques in order to detect conserved interactions while simultaneously attempting to maximize the sequence similarity of nodes involved in the alignment. We present an algorithm for multiple alignments, in which several PPI networks are aligned. We empirically evaluated our algorithm on three real biological datasets with 6 different species and found that our approach offers a significant benefit both in terms of quality as well as speed over the current state-of-the-art algorithms.</p> <p>Conclusion</p> <p>Computational experiments on the real datasets demonstrate that our multiple network alignment algorithm is a more efficient and effective algorithm than the state-of-the-art algorithm, IsoRankN. From a qualitative standpoint, our approach also offers a significant advantage over IsoRankN for the multiple network alignment problem.</p

    Simultaneous Optimization of Both Node and Edge Conservation in Network Alignment via WAVE

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    Network alignment can be used to transfer functional knowledge between conserved regions of different networks. Typically, existing methods use a node cost function (NCF) to compute similarity between nodes in different networks and an alignment strategy (AS) to find high-scoring alignments with respect to the total NCF over all aligned nodes (or node conservation). But, they then evaluate quality of their alignments via some other measure that is different than the node conservation measure used to guide the alignment construction process. Typically, one measures the amount of conserved edges, but only after alignments are produced. Hence, a recent attempt aimed to directly maximize the amount of conserved edges while constructing alignments, which improved alignment accuracy. Here, we aim to directly maximize both node and edge conservation during alignment construction to further improve alignment accuracy. For this, we design a novel measure of edge conservation that (unlike existing measures that treat each conserved edge the same) weighs each conserved edge so that edges with highly NCF-similar end nodes are favored. As a result, we introduce a novel AS, Weighted Alignment VotEr (WAVE), which can optimize any measures of node and edge conservation, and which can be used with any NCF or combination of multiple NCFs. Using WAVE on top of established state-of-the-art NCFs leads to superior alignments compared to the existing methods that optimize only node conservation or only edge conservation or that treat each conserved edge the same. And while we evaluate WAVE in the computational biology domain, it is easily applicable in any domain.Comment: 12 pages, 4 figure
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