期刊
MOLECULAR BIOSYSTEMS
卷 10, 期 4, 页码 820-830出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/c3mb70486f
关键词
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资金
- Polish Ministry of Education and Science [N301 159735, NCN 2013/09/B/NZ2/00121]
- University with Potential for Excellence (UPE) - Phase II project grant from University Grants Commission (UGC) in India
- Swedish Foundation for Strategic Research
- research fellowship within Project Information technologies: Research and their interdisciplinary applications [UDA-POKL.04.01.01-00-051/10-00]
Protein-protein interactions are important for the majority of biological processes. A significant number of computational methods have been developed to predict protein-protein interactions using protein sequence, structural and genomic data. Vast experimental data is publicly available on the Internet, but it is scattered across numerous databases. This fact motivated us to create and evaluate new high-throughput datasets of interacting proteins. We extracted interaction data from DIP, MINT, BioGRID and IntAct databases. Then we constructed descriptive features for machine learning purposes based on data from Gene Ontology and DOMINE. Thereafter, four well-established machine learning methods: Support Vector Machine, Random Forest, Decision Tree and Naive Bayes, were used on these datasets to build an Ensemble Learning method based on majority voting. In cross-validation experiment, sensitivity exceeded 80% and classification/prediction accuracy reached 90% for the Ensemble Learning method. We extended the experiment to a bigger and more realistic dataset maintaining sensitivity over 70%. These results confirmed that our datasets are suitable for performing PPI prediction and Ensemble Learning method is well suited for this task. Both the processed PPI datasets and the software are available at http://sysbio.icm.edu.pl/indra/EL-PPI/home.html.
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