ArticleMolecules (Basel, Switzerland)2018
A Central Edge Selection Based Overlapping Community Detection Algorithm for the Detection of Overlapping Structures in Protein⁻Protein Interaction Networks.
Article in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 16 citations in OpenAlex.
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- Research on the Computational Prediction of Essential Genes.Frontiers in cell and developmental biology · 2021Review
- Computational Prediction of Protein Arginine Methylation Based on Composition-Transition-Distribution Features.ACS omega · 2020Article
- A Method for Prediction of Thermophilic Protein Based on Reduced Amino Acids and Mixed Features.Frontiers in bioengineering and biotechnology · 2020Article
- Discrimination of Thermophilic Proteins and Non-thermophilic Proteins Using Feature Dimension Reduction.Frontiers in bioengineering and biotechnology · 2020Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
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Abstract
Overlapping structures of protein⁻protein interaction networks are very prevalent in different biological processes, which reflect the sharing mechanism to common functional components. The overlapping community detection (OCD) algorithm based on central node selection (CNS) is a traditional and acceptable algorithm for OCD in networks. The main content of CNS is the central node selection and the clustering procedure. However, the original CNS does not consider the influence among the nodes and the importance of the division of the edges in networks. In this paper, an OCD algorithm based on a central edge selection (CES) algorithm for detection of overlapping communities of protein⁻protein interaction (PPI) networks is proposed. Different from the traditional CNS algorithms for OCD, the proposed algorithm uses community magnetic interference (CMI) to obtain more reasonable central edges in the process of CES, and employs a new distance between the non-central edge and the set of the central edges to divide the non-central edge into the correct cluster during the clustering procedure. In addition, the proposed CES improves the strategy of overlapping nodes pruning (ONP) to make the division more precisely. The experimental results on three benchmark networks and three biological PPI networks of
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