ArticleNature communications2023
Assessment of community efforts to advance network-based prediction of protein-protein interactions.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- Dual-channel graph learning reveals similarity and complementarity in protein-protein interaction networks.PLoS computational biology · 2026Article
- Evaluating network-based algorithms for protein function prediction using plant protein-protein interaction networks.BMC plant biology · 2026Article
- Mass Spectrometry Proteomics: A Key to Faster Drug Discovery.Journal of medicinal chemistry · 2026Review
- Interactomics: Dissecting Protein Networks in Living Systems.Advances in experimental medicine and biology · 2026Review
- PF-AGCN: an adaptive graph convolutional network for protein-protein interaction-based function prediction.Bioinformatics (Oxford, England) · 2025Article
- Dual-protein embedding-based graph model with dynamic attention for interaction prediction.Briefings in bioinformatics · 2025Article
- Recent progress and future challenges in structure-based protein-protein interaction prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Heterogeneous network approaches to protein pathway prediction.Computational and structural biotechnology journal · 2024Review
- Introducing dysfunctional Protein-Protein Interactome (dfPPI) - A platform for systems-level protein-protein interaction (PPI) dysfunction investigation in disease.Current opinion in structural biology · 2024Review
- Genome-Wide Analysis of Aquaporins Gene Family inInternational journal of molecular sciences · 2024Article
- Homologous mapping yielded a comprehensive predicted protein-protein interaction network for peanut (Arachis hypogaea L.).BMC plant biology · 2024Article
- SENSE-PPI reconstructs interactomes within, across, and between species at the genome scale.iScience · 2024Article
- Pitfalls of machine learning models for protein-protein interaction networks.Bioinformatics (Oxford, England) · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- The axes of biology: a novel axes-based network embedding paradigm to decipher the functional mechanisms of the cell.Bioinformatics advances · 2024Article
- Unveiling the mechanisms of nephrotoxicity caused by nephrotoxic compounds using toxicological network analysis.Molecular therapy. Nucleic acids · 2023Article
- Dual-mechanism therapy for chronic rhinosinusitis via siRNA targeting of bacterial 16S rRNA and disruption of CFTR-pathogen interactions.Brazilian journal of otorhinolaryngologyArticle
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31 authors.
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Abstract
Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.
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