Noisy scale-free networks

Scholz, J.; Dejori, Mathäus; Stetter, M.; Greiner, Martin

doi:10.1016/j.physa.2004.11.012

Cited by 10 publications

(13 citation statements)

References 25 publications

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“…In particular, it is important to have a thorough understanding of the effect of missing or inaccurate data on the performance of centrality measures or other approaches to predicting essentiality. While there has been some research into this fundamental issue recently [42,202,29,163], more intensive quantitative and theoretical studies are needed before we can reliably apply the techniques discussed here to the problem of essentiality prediction. This issue is all the more important given that much of the data available on bio-molecular networks contains large numbers of false positive and false negative results [40,186].…”

Section: (Iii) Sensitivity To Data Errorsmentioning

confidence: 99%

Graph theory and networks in Biology

2007

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A survey of the use of graph theoretical techniques in Biology is presented. In particular, recent work on identifying and modelling the structure of bio-molecular networks is discussed, as well as the application of centrality measures to interaction networks and research on the hierarchical structure of such networks and network motifs. Work on the link between structural network properties and dynamics is also described, with emphasis on synchronisation and disease propagation.

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Section: (Iii) Sensitivity To Data Errorsmentioning

confidence: 99%

Graph theory and networks in Biology

2007

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“…However, we stongly belive that this Power law distribution is achieved because of the reduced number of links per node, and that increasing the connectivity of the network we will end up with a Poisson degree distribution. This hipotesis is very important because real neural topologies exhibit this type of degree distribution, and it is supported by the work presented in [18], where they have shown that the random adding of links to a scale-free network results in a Poisson degree distribution. Proving this hipotesis is the main raison for pursuing our research toward more highly connected networks.…”

Section: B Activity-driven Synaptogenesismentioning

confidence: 80%

Dynamic Routing on the Ubichip: Toward Synaptogenetic Neural Networks

Upegui¹,

Thoma²,

Pérez-Uribe³

et al. 2008

2008 NASA/ESA Conference on Adaptive Hardware and Systems

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The ubichip is a bio-inspired reconfigurable circuit developed in the framework of the european project Perplexus. The ubichip offers special reconfigurability capabilities, being the dynamic routing one of them. This paper describes how to exploit the dynamic routing capabilities of the ubichip in order to implement synaptogenetic neural networks. We present two techniques for dynamically generating the network topology, we describe their implementation in the ubichip, and we analyse the resulting topology. This work constitutes a first step toward neural circuits exhibiting more realistic neural plasticity features.

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“…Therefore, statistical properties obtained using incomplete datasets and low-quality data may not reproduce the real features of the underlying network [85,86]. This issue has been investigated in several studies, mainly focusing on data quality and noise sources of protein interaction data [81,82,[87][88][89]. In Han et al [89], the effect of sampling on structures of protein interaction networks was extensively investigated, and the authors concluded that, according to the current very low-density sample levels, the observed scale-free distribution of existing protein interaction graphs is likely an artifact, and therefore may not reflect the true structure of the complete interactome.…”

Section: Discussion and Summarymentioning

confidence: 99%

Recent Progress on the Analysis of Power-law Features in Complex Cellular Networks

Nacher

Akutsu

2007

Cell Biochem Biophys

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Complex interactions between different kinds of bio-molecules and essential nutrients are responsible for cellular functions. Rapid advances in theoretical modeling and experimental analyses have shown that drastically different biological and non-biological networks share a common architecture. That is, the probability that a selected node in the network has exactly k edges decays as a power-law. This finding has definitely opened an intense research and debate on the origin and implications of this ubiquitous pattern. In this review, we describe the recent progress on the emergence of power-law distributions in cellular networks. We first show the internal characteristics of the observed complex networks uncovered using graph theory. We then briefly review some works that have significantly contributed to the theoretical analysis of cellular networks and systems, from metabolic and protein networks to gene expression profiles. This prevalent topology observed in so many diverse biological systems suggests the existence of generic laws and organizing principles behind the cellular networks.

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Noisy scale-free networks

Cited by 10 publications

References 25 publications

Graph theory and networks in Biology

Graph theory and networks in Biology

Dynamic Routing on the Ubichip: Toward Synaptogenetic Neural Networks

Recent Progress on the Analysis of Power-law Features in Complex Cellular Networks

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