ArticleBMC bioinformatics2004
Predicting co-complexed protein pairs using genomic and proteomic data integration.
Article in BMC bioinformatics, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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57 citing papers in PubMed.
- A robust framework for protein-protein interaction prediction with multi-objective ensemble learning and embedding-based representations.Scientific reports · 2026Article
- Topology-driven negative sampling enhances generalizability in protein-protein interaction prediction.Bioinformatics (Oxford, England) · 2025Article
- Pitfalls of machine learning models for protein-protein interaction networks.Bioinformatics (Oxford, England) · 2024Article
- Molecular Modelling Hurdle in the Next-Generation Sequencing Era.International journal of molecular sciences · 2022Article
- Classification in biological networks with hypergraphlet kernels.Bioinformatics (Oxford, England) · 2021Article
- Protein Complexes Form a Basis for Complex Hybrid Incompatibility.Frontiers in genetics · 2021Review
- Improved cytokine-receptor interaction prediction by exploiting the negative sample space.BMC bioinformatics · 2020Article
- Protein-protein interaction prediction using a hybrid feature representation and a stacked generalization scheme.BMC bioinformatics · 2019Article
- ACCBN: ant-Colony-clustering-based bipartite network method for predicting long non-coding RNA-protein interactions.BMC bioinformatics · 2019Article
- A model to predict the function of hypothetical proteins through a nine-point classification scoring schema.BMC bioinformatics · 2019Article
- Cloning and Expression of the Organophosphate Pesticide-DegradingBioMed research international · 2018Article
- Survey of Natural Language Processing Techniques in Bioinformatics.Computational and mathematical methods in medicine · 2015Review
- PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction.International journal of genomics · 2015Article
- Integration strategy is a key step in network-based analysis and dramatically affects network topological properties and inferring outcomes.BioMed research international · 2014Article
- Protein complex identification by integrating protein-protein interaction evidence from multiple sources.PloS one · 2013Article
- Article
- A topology-based metric for measuring term similarity in the gene ontology.Advances in bioinformatics · 2012Article
- Computational prediction of protein-protein interactions in Leishmania predicted proteomes.PloS one · 2012Article
- A novel biclustering approach to association rule mining for predicting HIV-1-human protein interactions.PloS one · 2012Article
- Recovering protein-protein and domain-domain interactions from aggregation of IP-MS proteomics of coregulator complexes.PLoS computational biology · 2011Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying all protein-protein interactions in an organism is a major objective of proteomics. A related goal is to know which protein pairs are present in the same protein complex. High-throughput methods such as yeast two-hybrid (Y2H) and affinity purification coupled with mass spectrometry (APMS) have been used to detect interacting proteins on a genomic scale. However, both Y2H and APMS methods have substantial false-positive rates. Aside from high-throughput interaction screens, other gene- or protein-pair characteristics may also be informative of physical interaction. Therefore it is desirable to integrate multiple datasets and utilize their different predictive value for more accurate prediction of co-complexed relationship.
resultsUsing a supervised machine learning approach--probabilistic decision tree, we integrated high-throughput protein interaction datasets and other gene- and protein-pair characteristics to predict co-complexed pairs (CCP) of proteins. Our predictions proved more sensitive and specific than predictions based on Y2H or APMS methods alone or in combination. Among the top predictions not annotated as CCPs in our reference set (obtained from the MIPS complex catalogue), a significant fraction was found to physically interact according to a separate database (YPD, Yeast Proteome Database), and the remaining predictions may potentially represent unknown CCPs.
conclusionsWe demonstrated that the probabilistic decision tree approach can be successfully used to predict co-complexed protein (CCP) pairs from other characteristics. Our top-scoring CCP predictions provide testable hypotheses for experimental validation.
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