ArticleBioinformatics (Oxford, England)2009
Combining multiple positive training sets to generate confidence scores for protein-protein interactions.
Article in Bioinformatics (Oxford, England), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- An integrated bioinformatic analysis of microarray datasets to identify biomarkers and miRNA-based regulatory networks in leishmaniasis.Scientific reports · 2024Article
- Integration of probabilistic functional networks without an external Gold Standard.BMC bioinformatics · 2022Article
- Computational Network Inference for Bacterial Interactomics.mSystems · 2022Review
- Heterogeneous network propagation with forward similarity integration to enhance drug-target association prediction.PeerJ. Computer science · 2022Article
- Network Controllability-Based Prioritization of Candidates for SARS-CoV-2 Drug Repositioning.Viruses · 2020Article
- A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery.BMC bioinformatics · 2019Article
- Identification of potential gene targets in systemic vasculitis using DNA microarray analysis.Molecular medicine reports · 2017Article
- The integration of weighted human gene association networks based on link prediction.BMC systems biology · 2017Article
- Article
- Genome-wide protein-protein interactions and protein function exploration in cyanobacteria.Scientific reports · 2015Article
- Integrating the interactome and the transcriptome of Drosophila.BMC bioinformatics · 2014Article
- Reconstruction of the experimentally supported human protein interactome: what can we learn?BMC systems biology · 2013Article
- Mining breast cancer genes with a network based noise-tolerant approach.BMC systems biology · 2013Article
- Identification of new protein interactions between dengue fever virus and its hosts, human and mosquito.PloS one · 2013Article
- A computational framework for boosting confidence in high-throughput protein-protein interaction datasets.Genome biology · 2012Article
- A physical interaction network of dengue virus and human proteins.Molecular & cellular proteomics : MCP · 2011Article
- iRefR: an R package to manipulate the iRefIndex consolidated protein interaction database.BMC bioinformatics · 2011Article
- A protein network-guided screen for cell cycle regulators in Drosophila.BMC systems biology · 2011Article
- Improving the prediction of yeast protein function using weighted protein-protein interactions.Theoretical biology & medical modelling · 2011Article
- iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence.Database : the journal of biological databases and curation · 2010Article
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2 authors.
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Abstract
motivationHigh-throughput experimental and computational methods are generating a wealth of protein-protein interaction data for a variety of organisms. However, data produced by current state-of-the-art methods include many false positives, which can hinder the analyses needed to derive biological insights. One way to address this problem is to assign confidence scores that reflect the reliability and biological significance of each interaction. Most previously described scoring methods use a set of likely true positives to train a model to score all interactions in a dataset. A single positive training set, however, may be biased and not representative of true interaction space.
resultsWe demonstrate a method to score protein interactions by utilizing multiple independent sets of training positives to reduce the potential bias inherent in using a single training set. We used a set of benchmark yeast protein interactions to show that our approach outperforms other scoring methods. Our approach can also score interactions across data types, which makes it more widely applicable than many previously proposed methods. We applied the method to protein interaction data from both Drosophila melanogaster and Homo sapiens. Independent evaluations show that the resulting confidence scores accurately reflect the biological significance of the interactions.
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