A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction Networks Article Swipe
Jie Wang
,
Wenping Zheng
,
Yuhua Qian
,
Jiye Liang
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/molecules22122179
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/molecules22122179
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 u, we define its closeness to a cluster C, denoted as NC(u, C), by combing the density of a cluster C and the connection between a node u and C. In SEGC, a cluster which initially consists of only a seed node, is extended by adding nodes recursively from its neighbors according to the closeness, until all neighbors fail the process of expansion. We compare the F-measure and accuracy of the proposed SEGC algorithm with other algorithms on Saccharomyces cerevisiae protein interaction networks. The experimental results show that SEGC outperforms other algorithms under full coverage.
Related Topics
Concepts
Cluster analysis
Closeness
Computer science
Clustering coefficient
Node (physics)
Metric (unit)
Graph
Theoretical computer science
Topology (electrical circuits)
Data mining
Mathematics
Combinatorics
Artificial intelligence
Physics
Operations management
Economics
Quantum mechanics
Mathematical analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/molecules22122179
- https://www.mdpi.com/1420-3049/22/12/2179/pdf?version=1512987383
- OA Status
- gold
- Cited By
- 13
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2775711943
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2775711943Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/molecules22122179Digital Object Identifier
- Title
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A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction NetworksWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-12-08Full publication date if available
- Authors
-
Jie Wang, Wenping Zheng, Yuhua Qian, Jiye LiangList of authors in order
- Landing page
-
https://doi.org/10.3390/molecules22122179Publisher landing page
- PDF URL
-
https://www.mdpi.com/1420-3049/22/12/2179/pdf?version=1512987383Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1420-3049/22/12/2179/pdf?version=1512987383Direct OA link when available
- Concepts
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Cluster analysis, Closeness, Computer science, Clustering coefficient, Node (physics), Metric (unit), Graph, Theoretical computer science, Topology (electrical circuits), Data mining, Mathematics, Combinatorics, Artificial intelligence, Physics, Operations management, Economics, Quantum mechanics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1, 2023: 2, 2022: 1, 2021: 3Per-year citation counts (last 5 years)
- References (count)
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45Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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