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Transelliptical graphical models
 In Advances in Neural Information Processing Systems
, 2012
"... We advocate the use of a new distribution family—the transelliptical—for robust inference of high dimensional graphical models. The transelliptical family is an extension of the nonparanormal family proposed by Liu et al. (2009). Just as the nonparanormal extends the normal by transforming the varia ..."
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We advocate the use of a new distribution family—the transelliptical—for robust inference of high dimensional graphical models. The transelliptical family is an extension of the nonparanormal family proposed by Liu et al. (2009). Just as the nonparanormal extends the normal by transforming the variables using univariate functions, the transelliptical extends the elliptical family in the same way. We propose a nonparametric rankbased regularization estimator which achieves the parametric rates of convergence for both graph recovery and parameter estimation. Such a result suggests that the extra robustness and flexibility obtained by the semiparametric transelliptical modeling incurs almost no efficiency loss. We also discuss the relationship between this work with the transelliptical component analysis proposed by Han and Liu (2012). 1
GraMoFoNe: a Cytoscape plugin for querying motifs without topology in ProteinProtein Interactions networks
 In 2nd International Conference on Bioinformatics and Computational Biology (BICoB’10
, 2010
"... During the last decade, data on ProteinProtein Interactions (PPI) has increased in a huge manner. Searching for motifs in PPI Network has thus became a crucial problem to interpret this data. A large part of the literature is devoted to the query of motifs with a given topology. However, the biolog ..."
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During the last decade, data on ProteinProtein Interactions (PPI) has increased in a huge manner. Searching for motifs in PPI Network has thus became a crucial problem to interpret this data. A large part of the literature is devoted to the query of motifs with a given topology. However, the biological data are, by now, so noisy (missing and erroneous information) that the topology of a motif can be unrelevant. Consequently, Lacroix et al. [19] defined a new problem, called GRAPH MOTIF, which consists in searching a multiset of colors in a vertexcolored graph. In this article, we present GraMoFoNe, a plugin to Cytoscape based on a Linear PseudoBoolean optimization solver which handles GRAPH MOTIF and some of its extensions. 1.
Querying Graphs in ProteinProtein Interactions Networks using Feedback Vertex Set
"... Recent techniques increase rapidly the amount of our knowledge on interactions between proteins. The interpretation of these new information depends on our ability to retrieve known substructures in the data, the ProteinProtein Interactions (PPI) networks. In an algorithmic point of view, it is an ..."
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Cited by 3 (0 self)
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Recent techniques increase rapidly the amount of our knowledge on interactions between proteins. The interpretation of these new information depends on our ability to retrieve known substructures in the data, the ProteinProtein Interactions (PPI) networks. In an algorithmic point of view, it is an hard task since it often leads to NPhard problems. To overcome this difficulty, many authors have provided tools for querying patterns with a restricted topology, i.e. paths or trees in PPI networks. Such restriction leads to the development of fixed parameter tractable (FPT) algorithms, which can be practicable for restricted sizes of queries. Unfortunately, GRAPH HOMOMORPHISM is a W[1]hard problem, and hence, no FPT algorithm can be found when patterns are in the shape of general graphs. However, Dost et al. [2] gave an algorithm (which is not implemented) to query graphs with a bounded treewidth in PPI networks (the treewidth of the query being involved in the time complexity). In this paper, we propose another algorithm for querying pattern in the shape of graphs, also based on dynamic programming and the colorcoding technique. To transform graphs queries into trees without loss of informations, we use feedback vertex set coupled to a node duplication mecanism. Hence, our algorithm is FPT for querying graphs with a bounded size of their feedback vertex set. It gives an alternative to the treewidth parameter, which can be better or worst for a given query. We provide a python implementation which allows us to validate our implementation on real data. Especially, we retrieve some human queries in the shape of graphs into the fly PPI network.
1 2 Dividing Protein Interaction Networks for Modular Network Comparative Analysis ✩ 3 4
"... The increasing growth of data on proteinprotein interaction (PPI) networks has boosted research on their comparative analysis. In particular, recent studies proposed models and algorithms for performing network alignment, that is, the comparison of networks across species for discovering conserved ..."
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The increasing growth of data on proteinprotein interaction (PPI) networks has boosted research on their comparative analysis. In particular, recent studies proposed models and algorithms for performing network alignment, that is, the comparison of networks across species for discovering conserved functional complexes. In this paper, we present an algorithm for dividing PPI networks, prior to their alignment, into small subgraphs that are likely to cover conserved complexes. This allows one to perform network alignment in a modular fashion, by acting on pairs of resulting small subgraphs from different species. The proposed dividing algorithm combines a graph theoretical property (articulation) with a biological one (orthology). Extensive experiments on various PPI networks are conducted in order to assess how well the subgraphs generated by this dividing algorithm cover protein functional complexes and whether the proposed preprocessing step can be used for enhancing the performance of network alignment algorithms. Source code of the dividing algorithm is available upon request for academic use. Key words: protein interaction network division, modular network alignment