ArticleBMC bioinformatics2013
Protein complex detection using interaction reliability assessment and weighted clustering coefficient.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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64 citing papers in PubMed.
- TOMOC: a topology-driven multi-objective evolutionary framework for robust overlapping protein complex detection in noisy PPI networks.BMC bioinformatics · 2026Article
- Micro-functional protein complexes mining in biological intelligent computing: a weighted network approach.BMC bioinformatics · 2026Article
- miR-101-3p overexpression suppresses NSCLC progression through Immune-Related gene CALCRL regulation and lncRNA NEAT1 axis.Molecular biology reports · 2025Article
- EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks.Computational and structural biotechnology journal · 2025Article
- Graph-based self-supervised learning for repeat detection in metagenomic assembly.Genome research · 2024Article
- Spatiotemporal constrained RNA-protein heterogeneous network for protein complex identification.Briefings in bioinformatics · 2024Article
- Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis.Biomedicines · 2023Article
- Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and Obesity.Medicina (Kaunas, Lithuania) · 2023Article
- Bioinformatics Analysis of Next Generation Sequencing Data Identifies Molecular Biomarkers Associated With Type 2 Diabetes Mellitus.Clinical medicine insights. Endocrinology and diabetes · 2023Article
- Identification and Interaction Analysis of Molecular Markers in Pancreatic Ductal Adenocarcinoma by Bioinformatics and Next-Generation Sequencing Data Analysis.Bioinformatics and biology insights · 2023Article
- Small protein complex prediction algorithm based on protein-protein interaction network segmentation.BMC bioinformatics · 2022Article
- An integrated approach to understand fluid shear stress-driven and reactive oxygen species-mediated metastasis of colon adenocarcinoma through mRNA-miRNA-lncRNA-circRNA networks.Molecular genetics and genomics : MGG · 2022Article
- Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis.Scientific reports · 2022Article
- Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward.Computational and structural biotechnology journal · 2022Review
- Integrated bioinformatics analysis reveals novel key biomarkers in diabetic nephropathy.SAGE open medicine · 2022Article
- Discovering Common Pathophysiological Processes between COVID-19 and Cystic Fibrosis by Differential Gene Expression Pattern Analysis.BioMed research international · 2022Article
- An Ensemble Learning Framework for Detecting Protein Complexes From PPI Networks.Frontiers in genetics · 2022Article
- Identification of Potential Key Genes and Molecular Mechanisms of Medulloblastoma Based on Integrated Bioinformatics Approach.BioMed research international · 2022Article
- Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms' representation.BioData mining · 2021Article
- Shared Molecular Mechanisms of Hypertrophic Cardiomyopathy and Its Clinical Presentations: Automated Molecular Mechanisms Extraction Approach.Life (Basel, Switzerland) · 2021Article
4 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
backgroundPredicting protein complexes from protein-protein interaction data is becoming a fundamental problem in computational biology. The identification and characterization of protein complexes implicated are crucial to the understanding of the molecular events under normal and abnormal physiological conditions. On the other hand, large datasets of experimentally detected protein-protein interactions were determined using High-throughput experimental techniques. However, experimental data is usually liable to contain a large number of spurious interactions. Therefore, it is essential to validate these interactions before exploiting them to predict protein complexes.
resultsIn this paper, we propose a novel graph mining algorithm (PEWCC) to identify such protein complexes. Firstly, the algorithm assesses the reliability of the interaction data, then predicts protein complexes based on the concept of weighted clustering coefficient. To demonstrate the effectiveness of the proposed method, the performance of PEWCC was compared to several methods. PEWCC was able to detect more matched complexes than any of the state-of-the-art methods with higher quality scores.
conclusionsThe higher accuracy achieved by PEWCC in detecting protein complexes is a valid argument in favor of the proposed method. The datasets and programs are freely available at http://faculty.uaeu.ac.ae/nzaki/Research.htm.
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