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614
Reverse engineering of regulatory networks in human B cells.
 Nat. Genet.
, 2005
"... Cellular phenotypes are determined by the differential activity of networks linking coregulated genes. Available methods for the reverse engineering of such networks from genomewide expression profiles have been successful only in the analysis of lower eukaryotes with simple genomes. Using a new m ..."
Abstract

Cited by 178 (2 self)
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). Although scalefree networks may represent a common blueprint for all cellular constituents, evidence of scalefree topology in higherorder eukaryotic cells is currently limited to coexpression networks 3,4 , which tend to identify entire subpathways rather than individual interactions. Identifying
Dualistic geometry of the manifold of higherorder neurons
 Neural Networks
, 1991
"... Abstract Recursive Fractal Genome Function in the geometric mind frame of Tensor Network Theory (TNT) leads through FractoGene to a mathematical unification of physiological and pathological development of neural structure and function as governed by the genome. The cerebellum serves as the best pl ..."
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Cited by 25 (6 self)
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Abstract Recursive Fractal Genome Function in the geometric mind frame of Tensor Network Theory (TNT) leads through FractoGene to a mathematical unification of physiological and pathological development of neural structure and function as governed by the genome. The cerebellum serves as the best
Lassig M: Local graph alignment and motif search in biological networks
 PNAS
"... Interaction networks are of central importance in postgenomic molecular biology, with increasing amounts of data becoming available by highthroughput methods. Examples are gene regulatory networks or protein interaction maps. The main challenge in the analysis of these data is to read off biologica ..."
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Cited by 74 (1 self)
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biological functions from the topology of the network. Topological motifs, i.e., patterns occurring repeatedly at different positions in the network have recently been identified as basic modules of molecular information processing. In this paper, we discuss motifs derived from families of mutually similar
Scalable, Graphbased Network Vulnerability Analysis,”
 Proceedings of the 9th ACM Conference on Computer and Communications Security,
, 2002
"... ABSTRACT Even well administered networks are vulnerable to attack. Recent work in network security has focused on the fact that combinations of exploits are the typical means by which an attacker breaks into a network. Researchers have proposed a variety of graphbased algorithms to generate attack ..."
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Cited by 152 (0 self)
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of work uses a modified version of the model checker NuSMV as a powerful inference engine for chaining together network exploits, compactly representing attack graphs, and identifying minimal sets of exploits. However, it is also well known that model checkers suffer from scalability problems
Levchenko A: Dynamic Properties of Network Motifs Contribute to Biological Network Organization
 PLoS Biol
"... Biological networks, such as those describing gene regulation, signal transduction, and neural synapses, are representations of largescale dynamic systems. Discovery of organizing principles of biological networks can be enhanced by embracing the notion that there is a deep interplay between networ ..."
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Cited by 45 (0 self)
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—stability or robustness to small perturbations—is highly correlated with the relative abundance of small subnetworks (network motifs) in several previously determined biological networks. We propose that robust dynamical stability is an influential property that can determine the nonrandom structure of biological
Biological network motif detection and evaluation
, 2011
"... Background: Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as overrepresented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves co ..."
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Cited by 3 (0 self)
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Background: Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as overrepresented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves
Learning Markov Logic Networks Using Structural Motifs
"... Markov logic networks (MLNs) use firstorder formulas to define features of Markov networks. Current MLN structure learners can only learn short clauses (45 literals) due to extreme computational costs, and thus are unable to represent complex regularities in data. To address this problem, we presen ..."
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Cited by 43 (4 self)
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Markov logic networks (MLNs) use firstorder formulas to define features of Markov networks. Current MLN structure learners can only learn short clauses (45 literals) due to extreme computational costs, and thus are unable to represent complex regularities in data. To address this problem, we
Relational dependency networks
 Journal of Machine Learning Research
, 2007
"... Recent work on graphical models for relational data has demonstrated significant improvements in classification and inference when models represent the dependencies among instances. Despite its use in conventional statistical models, the assumption of instance independence is contradicted by most re ..."
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Cited by 113 (24 self)
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Recent work on graphical models for relational data has demonstrated significant improvements in classification and inference when models represent the dependencies among instances. Despite its use in conventional statistical models, the assumption of instance independence is contradicted by most
Recurrent structural motifs reflect characteristics of distinct networks
"... Abstract—In largescale networks, certain topological patterns may occur more frequently than expected from a null model that preserves global (such as the density of the graph) and local (such as the connectivity of each node) properties of the graph. These network motifs are the building blocks of ..."
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of largescale networks and may confer functional/mechanistic implications of their underlying processes. Despite active investigations and rich literature in systems biology, network motifs are less explored in social network studies. In this work, we modified and improved the method from Milo et al. 2002
Using network motifs to identify application protocols
 In Proceedings of the 28th IEEE Conference on Global Telecommunications, GLOBECOM’09
, 2009
"... Abstract—Identifying application types in network traffic is a difficult problem for administrators who must secure and manage network resources, further complicated by the use of encrypted protocols and nonstandard port numbers. This paper takes a unique approach to this problem by modeling and ana ..."
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Cited by 7 (1 self)
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and analyzing application graphs, structures which describe the applicationlevel (e.g., HTTP, FTP) communications between hosts. These graphs are searched for motifs: recurring, significant patterns of interconnections that can be used to help determine the network application in use. Motifbased analysis has
Results 1  10
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614