An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality

Yoon, Jeongah; Blumer, Anselm; Lee, Kyongbum

doi:10.1093/bioinformatics/btl533

Cited by 211 publications

(146 citation statements)

References 8 publications

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“…Betweenness centrality describes the level of control that one node exerts over the interaction of other nodes in a network (55). Information about the betweenness centrality score, for each protein in the global networks for murine PPP-or PPG-rich proteins is available in supplemental Tables 3 and 4.…”

Section: Identification Of Protein Complexes and Biological Processesmentioning

confidence: 99%

Biological Relevance and Therapeutic Potential of the Hypusine Modification System

Pällmann

Braig

Sievert

et al. 2015

Journal of Biological Chemistry

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Background: Hypusine modification of the eukaryotic initiation factor 5A (eIF-5A) represents a conserved post-translational modification that regulates translation. Results: Deletion of hypusine modification enzymes exerts strong phenotypes. eIF-5A2-deleted animals are viable and fertile. Conclusion: Both enzymatic steps of hypusine modification are essential for mammalian homeostasis, whereas the cancerrelated isoform eIF-5A2 is dispensable. Significance: eIF-5A2 might represent a safe therapeutic target.

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Section: Identification Of Protein Complexes and Biological Processesmentioning

confidence: 99%

Biological Relevance and Therapeutic Potential of the Hypusine Modification System

Pällmann

Braig

Sievert

et al. 2015

Journal of Biological Chemistry

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“…This property of a network reflects the amount of control that a node exerts over the interactions of other nodes in the network (Yoon, Blumer, & Lee, 2006). The measure of betweenness centrality rewards nodes that are part of communities, rather than nodes that lie inside a community.…”

Section: Definition and Characteristics Of A Networkmentioning

confidence: 99%

Global technological collaboration network: network analysis of international co-inventions

Prato

Nepelski

2012

J Technol Transf

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Global innovation networks are emerging as a result of the international division of innovation processes through, among others, international technological collaborations. At the aggregate level, the creation of technological collaboration between countries can be considered as mutually beneficial (or detrimental) and their random distribution is unlikely. Consequently, the dynamics and evolution of the technological collaborations can be expected to fulfil the criteria of a complex network. To study the structure and evolution of the global technological collaboration network, we use patent-based data of international co-inventions and apply the network analysis. In addition, extending the gravity model of international technological collaboration by measures controlling for countries position in the network, we show that that a country's position in the network has very strong impact on the intensity of collaboration with other members of the network.

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“…It is important to note that there are a number of alternative algorithmic techniques for the detection of communities in networks. They include hierarchical clustering, partitioning graphs to maximize quality functions such as network modularity, k-clique percolation, and some other interesting algorithmic methods [1,2]. Nevertheless, the Newman and Girvan conceptual system is often chosen as a pretext for scientific contributions due to its structural articulation and its ability to be used in a wide range of practical situations [3].…”

Section: Essential Previous Workmentioning

confidence: 99%

The Flag-based Algorithm - A Novel Greedy Method that Optimizes Protein Communities Detection

Bocu

Tabirca

2011

INT J COMPUT COMMUN

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Proteins and the networks they determine, called interactome networks, have received attention at an important degree during the last years, because they have been discovered to have an influence on some complex biological phenomena, such as problematic disorders like cancer. This paper presents a contribution that aims to optimize the detection of protein communities through a greedy algorithm that is implemented in the C programming language. The optimization involves a double improvement in relation to protein communities detection, which is accomplished both at the algorithmic and programming level. The resulting implementation's performance was carefully tested on real biological data and the results acknowledge the relevant speedup that the optimization determines. Moreover, the results are in line with the previous findings that our current research produced, as it reveals and confirms the existence of some important properties of those proteins that participate in the carcinogenesis process. Apart from being particularly useful for research purposes, the novel community detection algorithm also dramatically speeds up the proteomic databases analysis process, as compared to some other sequential community detection approaches, and also to the sequential algorithm of Newman and Girvan.

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An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality

Cited by 211 publications

References 8 publications

Biological Relevance and Therapeutic Potential of the Hypusine Modification System

Biological Relevance and Therapeutic Potential of the Hypusine Modification System

Global technological collaboration network: network analysis of international co-inventions

The Flag-based Algorithm - A Novel Greedy Method that Optimizes Protein Communities Detection

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