ArticleScientific reports2017
Identifying protein complexes in PPI network using non-cooperative sequential game.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Challenges and opportunities in network-based solutions for biological questions.Briefings in bioinformatics · 2022Article
- Article
- Detecting Protein Communities in Native Cell Extracts by Machine Learning: A Structural Biologist's Perspective.Frontiers in molecular biosciences · 2021Article
- Detection of dynamic protein complexes through Markov Clustering based on Elephant Herd Optimization Approach.Scientific reports · 2019Article
- Validation and quality assessment of macromolecular structures using complex network analysis.Scientific reports · 2019Article
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3 authors.
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Abstract
Identifying protein complexes from protein-protein interaction (PPI) network is an important and challenging task in computational biology as it helps in better understanding of cellular mechanisms in various organisms. In this paper we propose a noncooperative sequential game based model for protein complex detection from PPI network. The key hypothesis is that protein complex formation is driven by mechanism that eventually optimizes the number of interactions within the complex leading to dense subgraph. The hypothesis is drawn from the observed network property named small world. The proposed multi-player game model translates the hypothesis into the game strategies. The Nash equilibrium of the game corresponds to a network partition where each protein either belong to a complex or form a singleton cluster. We further propose an algorithm to find the Nash equilibrium of the sequential game. The exhaustive experiment on synthetic benchmark and real life yeast networks evaluates the structural as well as biological significance of the network partitions.
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