Article: Protein complex prediction based on dense sub-graph merging Journal: International Journal of Data Mining and Bioinformatics (IJDMB) 2021 Vol.26 No.3/4 pp.129 - 150 Abstract: Protein complex prediction is an essential task in cell biology to understand and analyse the Protein-Protein Interaction (PPI) networks, further bringing about the knowledge of many important biological functions. The availability of ever-growing protein-protein interaction datasets and the inherent limitation of the experimental methods for protein complex prediction ask for efficient computational approaches. In this article, the authors presented a PROtein COmplex Prediction (PROCOP) technique based on dense sub-graph merging, which considers the inherent organisation of proteins and the regions with heavy interactions in PPI networks. The work is intended to isolate the dense regions of the PPI network by simply a neighbourhood search, followed by a merging strategy based on the weighted cluster density. Two or more dense regions are merged iteratively to produce biologically meaningful protein complexes. The predicted protein complexes are evaluated and analysed using the PPI network of Saccharomyces cerevisiae and Homo sapiens. The performance of the proposed algorithm is at par with most of the existing algorithms and outperforms in terms of evaluation metrics like F-measure and accuracy. Inderscience Publishers - linking academia, business and industry through research

Title: Protein complex prediction based on dense sub-graph merging

Authors: Tushar Ranjan Sahoo; Swati Vipsita; Sabyasachi Patra

Addresses: Bioinformatics Lab, Department of Computer Science, International Institute of Information Technology, Bhubaneswar (IIIT Bhubaneswar), Bhubaneswar, Odisha, India ' Bioinformatics Lab, Department of Computer Science, International Institute of Information Technology, Bhubaneswar (IIIT Bhubaneswar), Bhubaneswar, Odisha, India ' Bioinformatics Lab, Department of Computer Science, International Institute of Information Technology, Bhubaneswar (IIIT Bhubaneswar), Bhubaneswar, Odisha, India

Abstract: Protein complex prediction is an essential task in cell biology to understand and analyse the Protein-Protein Interaction (PPI) networks, further bringing about the knowledge of many important biological functions. The availability of ever-growing protein-protein interaction datasets and the inherent limitation of the experimental methods for protein complex prediction ask for efficient computational approaches. In this article, the authors presented a PROtein COmplex Prediction (PROCOP) technique based on dense sub-graph merging, which considers the inherent organisation of proteins and the regions with heavy interactions in PPI networks. The work is intended to isolate the dense regions of the PPI network by simply a neighbourhood search, followed by a merging strategy based on the weighted cluster density. Two or more dense regions are merged iteratively to produce biologically meaningful protein complexes. The predicted protein complexes are evaluated and analysed using the PPI network of Saccharomyces cerevisiae and Homo sapiens. The performance of the proposed algorithm is at par with most of the existing algorithms and outperforms in terms of evaluation metrics like F-measure and accuracy.

Keywords: biological network; protein complex; induced sub-graph; sub-graph merging; clustering.

DOI: 10.1504/IJDMB.2021.126837

International Journal of Data Mining and Bioinformatics, 2021 Vol.26 No.3/4, pp.129 - 150

Received: 17 Sep 2021
Accepted: 26 May 2022
Published online: 08 Nov 2022 *

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Title: Protein complex prediction based on dense sub-graph merging

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