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Identification of Important Nodes in Directed Biological Networks: A Network Motif Approach

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  • Pei Wang
  • Jinhu Lü
  • Xinghuo Yu

Abstract

Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA), this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC) curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine.

Suggested Citation

  • Pei Wang & Jinhu Lü & Xinghuo Yu, 2014. "Identification of Important Nodes in Directed Biological Networks: A Network Motif Approach," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-15, August.
  • Handle: RePEc:plo:pone00:0106132
    DOI: 10.1371/journal.pone.0106132
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    References listed on IDEAS

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    1. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
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    Cited by:

    1. Satyaki Roy & Ahmad F. Al Musawi & Preetam Ghosh, 2023. "Inferring links in directed complex networks through feed forward loop motifs," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    2. Hongping Wang & Yajuan Zhang & Zili Zhang & Sankaran Mahadevan & Yong Deng, 2015. "PhysarumSpreader: A New Bio-Inspired Methodology for Identifying Influential Spreaders in Complex Networks," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-21, December.

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