ArticleBioinformatics (Oxford, England)2013
A novel link prediction algorithm for reconstructing protein-protein interaction networks by topological similarity.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Graphlet- and motif-based link prediction in large networks.Patterns (New York, N.Y.) · 2026Article
- Hypergraph representations of single-cell RNA sequencing data for improved cell clustering.Bioinformatics (Oxford, England) · 2026Article
- Article
- Bias-aware training and evaluation of link prediction algorithms in network biology.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Ensemble decision of local similarity indices on the biological network for disease related gene prediction.PeerJ · 2024Article
- Hypergraph reconstruction from uncertain pairwise observations.Scientific reports · 2023Article
- The importance of graph databases and graph learning for clinical applications.Database : the journal of biological databases and curation · 2023Review
- A link prediction-based recommendation system using transactional data.Scientific reports · 2023Article
- Normalized L3-based link prediction in protein-protein interaction networks.BMC bioinformatics · 2023Article
- Using PlaPPISite to Predict and Analyze Plant Protein-Protein Interaction Sites.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Data-Driven Technology Roadmaps to Identify Potential Technology Opportunities for Hyperuricemia Drugs.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Progresses and challenges in link prediction.iScience · 2021Review
- D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions.Cell systems · 2021Article
- An In Silico Method for Predicting Drug Synergy Based on Multitask Learning.Interdisciplinary sciences, computational life sciences · 2021Article
- Performance Assessment of the Network Reconstruction Approaches on Various Interactomes.Frontiers in molecular biosciences · 2021Article
- SkipGNN: predicting molecular interactions with skip-graph networks.Scientific reports · 2020Article
- GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks.Bioinformatics (Oxford, England) · 2020Article
- Computational Prediction of Probable Single Nucleotide Polymorphism-Cancer Relationships.Cancer informatics · 2020Article
- Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network.PloS one · 2020Article
- WIPER: Weighted in-Path Edge Ranking for biomolecular association networks.Quantitative biology (Beijing, China) · 2019Article
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Authors and funding
2 authors.
Funding
Abstract
motivationRecent advances in technology have dramatically increased the availability of protein-protein interaction (PPI) data and stimulated the development of many methods for improving the systems level understanding the cell. However, those efforts have been significantly hindered by the high level of noise, sparseness and highly skewed degree distribution of PPI networks. Here, we present a novel algorithm to reduce the noise present in PPI networks. The key idea of our algorithm is that two proteins sharing some higher-order topological similarities, measured by a novel random walk-based procedure, are likely interacting with each other and may belong to the same protein complex.
resultsApplying our algorithm to a yeast PPI network, we found that the edges in the reconstructed network have higher biological relevance than in the original network, assessed by multiple types of information, including gene ontology, gene expression, essentiality, conservation between species and known protein complexes. Comparison with existing methods shows that the network reconstructed by our method has the highest quality. Using two independent graph clustering algorithms, we found that the reconstructed network has resulted in significantly improved prediction accuracy of protein complexes. Furthermore, our method is applicable to PPI networks obtained with different experimental systems, such as affinity purification, yeast two-hybrid (Y2H) and protein-fragment complementation assay (PCA), and evidence shows that the predicted edges are likely bona fide physical interactions. Finally, an application to a human PPI network increased the coverage of the network by at least 100%. AVAILABILITY: www.cs.utsa.edu/∼jruan/RWS/.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.