ReviewComputational and structural biotechnology journal2022
Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward.
Review in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Mapping Dysfunctional Protein-Protein Interactions in Disease.Journal of visualized experiments : JoVE · 2025Article
- RNA-Seq Uncovers Association of Endocrine-Disrupting Chemicals with Hub Genes and Transcription Factors in Aggressive Prostate Cancer.International journal of molecular sciences · 2025Article
- Unveiling biomarker detection in Alzheimer's disease: a computational approach to microarray analysis.3 Biotech · 2024Article
- Multi-source biological knowledge-guided hypergraph spatiotemporal subnetwork embedding for protein complex identification.Briefings in bioinformatics · 2024Article
- Integration of protein sequence and protein-protein interaction data by hypergraph learning to identify novel protein complexes.Briefings in bioinformatics · 2024Article
- The Complementarity of Nuclear Magnetic Resonance and Native Mass Spectrometry in Probing Protein-Protein Interactions.Advances in experimental medicine and biology · 2024Review
- LIRcentral: a manually curated online database of experimentally validated functional LIR motifs.Autophagy · 2023Article
- Employing non-targeted interactomics approach and subcellular fractionation to increase our understanding of the ghost proteome.iScience · 2023Article
- Modelling speciation: Problems and implications.In silico biology · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Physically interacting proteins form macromolecule complexes that drive diverse cellular processes. Advances in experimental techniques that capture interactions between proteins provide us with protein-protein interaction (PPI) networks from several model organisms. These datasets have enabled the prediction and other computational analyses of protein complexes. Here we provide a systematic review of the state-of-the-art algorithms for protein complex prediction from PPI networks proposed in the past two decades. The existing approaches that solve this problem are categorized into three groups, including: cluster-quality-based, node affinity-based, and network embedding-based approaches, and we compare and contrast the advantages and disadvantages. We further include a comparative analysis by computing the performance of eighteen methods based on twelve well-established performance measures on four widely used benchmark protein-protein interaction networks. Finally, the limitations and drawbacks of both, current data and approaches, along with the potential solutions in this field are discussed, with emphasis on the points that pave the way for future research efforts in this field.
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