Cohesive Sub-network Mining in Protein Interaction Networks Using Score-Based Co-clustering with MapReduce Model (MR-CoC)

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 645)

Abstract

Nowadays, due to the data deluge situation, every computation has to be carried out in voluminous data. The sub-network mining from the complex and voluminous interaction data is one of the research challenges. The highly connected sub-networks will be more cohesive in the network. They are responsible for communication among the network, which is useful for studying their functionalities. A novel score-based co-clustering (MR-CoC) technique with MapReduce is proposed to mine the highly connected sub-network from interaction networks. The MapReduce environment is chosen to cope with complex, voluminous data and to parallelize the computation process. This approach is used to mine cliques, non-cliques, and overlapping sub-network patterns from the adjacency matrix of the network. The complexity of the proposed work is O (Es + log Ns), which is minimal than the existing approaches like MCODE and spectral clustering.

Keywords

MapReduce Clustering Protein interaction network Co-clustering Functional coherence Sub-network mining Distributed computing Big data 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer SciencePeriyar UniversitySalemIndia

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