ArticleMolecules (Basel, Switzerland)2017
A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction Networks.
Article in Molecules (Basel, Switzerland), 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.
- Micro-functional protein complexes mining in biological intelligent computing: a weighted network approach.BMC bioinformatics · 2026Article
- A graph clustering algorithm with hypergraph learning and a core-attachment strategy for protein complex identification.Frontiers in genetics · 2026Article
- Exploring the biological tissue toxicity and related toxic components of Euodiae Fructus through the multi-tissue spectrum-toxicity relationship.Journal of natural medicines · 2025Article
- Study on the mechanism of Gastrodiae Rhizoma, Lycii Fructus, and Ziziphi Spinosae Semen in sedation and tranquillising mind.Molecular diversity · 2024Article
- Modularity maximization to design contiguous policy zones for pandemic response.European journal of operational research · 2023Article
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Authors and funding
4 authors.
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Abstract
Most proteins perform their biological functions while interacting as complexes. The detection of protein complexes is an important task not only for understanding the relationship between functions and structures of biological network, but also for predicting the function of unknown proteins. We present a new nodal metric by integrating its local topological information. The metric reflects its representability in a larger local neighborhood to a cluster of a protein interaction (PPI) network. Based on the metric, we propose a seed-expansion graph clustering algorithm (SEGC) for protein complexes detection in PPI networks. A roulette wheel strategy is used in the selection of the seed to enhance the diversity of clustering. For a candidate node
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