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BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes

Author

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  • Amir Lakizadeh
  • Saeed Jalili

Abstract

Considering the roles of protein complexes in many biological processes in the cell, detection of protein complexes from available protein-protein interaction (PPI) networks is a key challenge in the post genome era. Despite high dynamicity of cellular systems and dynamic interaction between proteins in a cell, most computational methods have focused on static networks which cannot represent the inherent dynamicity of protein interactions. Recently, some researchers try to exploit the dynamicity of PPI networks by constructing a set of dynamic PPI subnetworks correspondent to each time-point (column) in a gene expression data. However, many genes can participate in multiple biological processes and cellular processes are not necessarily related to every sample, but they might be relevant only for a subset of samples. So, it is more interesting to explore each subnetwork based on a subset of genes and conditions (i.e., biclusters) in a gene expression data. Here, we present a new method, called BiCAMWI to employ dynamicity in detecting protein complexes. The preprocessing phase of the proposed method is based on a novel genetic algorithm that extracts some sets of genes that are co-regulated under some conditions from input gene expression data. Each extracted gene set is called bicluster. In the detection phase of the proposed method, then, based on the biclusters, some dynamic PPI subnetworks are extracted from input static PPI network. Protein complexes are identified by applying a detection method on each dynamic PPI subnetwork and aggregating the results. Experimental results confirm that BiCAMWI effectively models the dynamicity inherent in static PPI networks and achieves significantly better results than state-of-the-art methods. So, we suggest BiCAMWI as a more reliable method for protein complex detection.

Suggested Citation

  • Amir Lakizadeh & Saeed Jalili, 2016. "BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-16, July.
  • Handle: RePEc:plo:pone00:0159923
    DOI: 10.1371/journal.pone.0159923
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    References listed on IDEAS

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    1. Le Ou-Yang & Dao-Qing Dai & Xiao-Fei Zhang, 2013. "Protein Complex Detection via Weighted Ensemble Clustering Based on Bayesian Nonnegative Matrix Factorization," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-18, May.
    2. Neng Fan & Nikita Boyko & Panos M. Pardalos, 2010. "Recent Advances of Data Biclustering with Application in Computational Neuroscience," Springer Optimization and Its Applications, in: Wanpracha Chaovalitwongse & Panos M. Pardalos & Petros Xanthopoulos (ed.), Computational Neuroscience, chapter 0, pages 85-112, Springer.
    3. Jing-Dong J. Han & Nicolas Bertin & Tong Hao & Debra S. Goldberg & Gabriel F. Berriz & Lan V. Zhang & Denis Dupuy & Albertha J. M. Walhout & Michael E. Cusick & Frederick P. Roth & Marc Vidal, 2004. "Erratum: Evidence for dynamically organized modularity in the yeast protein–protein interaction network," Nature, Nature, vol. 430(6997), pages 380-380, July.
    4. Jing-Dong J. Han & Nicolas Bertin & Tong Hao & Debra S. Goldberg & Gabriel F. Berriz & Lan V. Zhang & Denis Dupuy & Albertha J. M. Walhout & Michael E. Cusick & Frederick P. Roth & Marc Vidal, 2004. "Evidence for dynamically organized modularity in the yeast protein–protein interaction network," Nature, Nature, vol. 430(6995), pages 88-93, July.
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    1. Dhuha Abdulhadi Abduljabbar & Siti Zaiton Mohd Hashim & Roselina Sallehuddin, 2020. "Nature-inspired optimization algorithms for community detection in complex networks: a review and future trends," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 74(2), pages 225-252, June.

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