Treffer: A Review on Efficient and Scalable Graph-Based Clustering Algorithms for Protein Complex Identification in PPI Networks.
Title:
A Review on Efficient and Scalable Graph-Based Clustering Algorithms for Protein Complex Identification in PPI Networks.
Authors:
Source:
Proteins [Proteins] 2026 Feb; Vol. 94 (2), pp. 477-501. Date of Electronic Publication: 2025 Aug 17.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Wiley-Liss Country of Publication: United States NLM ID: 8700181 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0134 (Electronic) Linking ISSN: 08873585 NLM ISO Abbreviation: Proteins Subsets: MEDLINE
Imprint Name(s):
Publication: New York, NY : Wiley-Liss
Original Publication: New York : Alan R. Liss, c1986-
Original Publication: New York : Alan R. Liss, c1986-
MeSH Terms:
References:
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Contributed Indexing:
Keywords: PPI network; cluster density; clustering; graph; protein complex
Substance Nomenclature:
0 (Proteins)
Entry Date(s):
Date Created: 20250817 Date Completed: 20260107 Latest Revision: 20260107
Update Code:
20260130
DOI:
10.1002/prot.70026
PMID:
40820243
Database:
MEDLINE
Weitere Informationen
Network clustering is employed in bioinformatics and data mining studies to investigate the structural and functional properties of protein-protein interaction (PPI) networks. In multiple studies over the past two decades, network clustering has proven valuable for uncovering functional modules and elucidating the functions of previously undiscovered proteins. Protein complexes are vital cellular components that play a crucial role in generating biological activity. Experimental techniques have inherent limitations in inferring protein complexes. Given these constraints, numerous computational methods have emerged over the past decade for predicting protein complexes. Typically, these methods take the input PPI data and generate predicted protein complexes as output subnetworks. Most of these methods have shown encouraging outcomes in predicting protein complexes. Prediction is challenging for sparse, small, and overlapping complexes. New strategies should include explicit knowledge about the biological characteristics of proteins to increase performance. Furthermore, specific issues should be considered more effectively in the future while developing new complex prediction algorithms. The bioinformatics community has developed various techniques for clustering PPI networks, which we identified, analyzed, and compared in this paper. This review evaluates various graph clustering algorithms for protein complex identification, facilitating the benchmarking of existing methods, identifying limitations, motivating the development of novel computational tools, and ultimately improving biological insight and therapeutic progress. Through the assessment of strengths and limitations, researchers may develop efficient and scalable algorithms designed explicitly for biological data, integrating graph-based methodologies with machine learning and deep learning approaches. This study is an invaluable tool for new researchers in the area to recognize upcoming trends, including dynamic PPI networks and temporal complex identification.
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