ArticleBMC bioinformatics2015
Protein complex detection in PPI networks based on data integration and supervised learning method.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 17 citations in OpenAlex.
- HMNPPID-human malignant neoplasm protein-protein interaction database.Human genomics · 2019Article
- Identifying protein complexes based on node embeddings obtained from protein-protein interaction networks.BMC bioinformatics · 2018Article
- Identification of risk genes associated with myocardial infarction based on the recursive feature elimination algorithm and support vector machine classifier.Molecular medicine reports · 2018Article
- Predicting protein complexes using a supervised learning method combined with local structural information.PloS one · 2018Article
- Discovering overlapped protein complexes from weighted PPI networks by removing inter-module hubs.Scientific reports · 2017Article
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Authors and funding
6 authors at 2 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundRevealing protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict protein complexes from protein-protein interaction (PPI) networks. However, the small amount of known physical interactions may limit protein complex detection.
methodsThe new PPI networks are constructed by integrating PPI datasets with the large and readily available PPI data from biomedical literature, and then the less reliable PPI between two proteins are filtered out based on semantic similarity and topological similarity of the two proteins. Finally, the supervised learning protein complex detection (SLPC), which can make full use of the information of available known complexes, is applied to detect protein complex on the new PPI networks.
resultsThe experimental results of SLPC on two different categories yeast PPI networks demonstrate effectiveness of the approach: compared with the original PPI networks, the best average improvements of 4.76, 6.81 and 15.75 percentage units in the F-score, accuracy and maximum matching ratio (MMR) are achieved respectively; compared with the denoising PPI networks, the best average improvements of 3.91, 4.61 and 12.10 percentage units in the F-score, accuracy and MMR are achieved respectively; compared with ClusterONE, the start-of the-art complex detection method, on the denoising extended PPI networks, the average improvements of 26.02 and 22.40 percentage units in the F-score and MMR are achieved respectively.
conclusionsThe experimental results show that the performances of SLPC have a large improvement through integration of new receivable PPI data from biomedical literature into original PPI networks and denoising PPI networks. In addition, our protein complexes detection method can achieve better performance than ClusterONE.
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