ArticleCellular & molecular biology letters2011
PPI_SVM: prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables.
Article in Cellular & molecular biology letters, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Article
- GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs.BMC genomics · 2024Article
- Advances in the Applications of Bioinformatics and Chemoinformatics.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Oral_voting_transfer: classification of oral microorganisms' function proteins with voting transfer model.Frontiers in microbiology · 2023Article
- PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms.Frontiers in genetics · 2022Article
- RF_phage virion: Classification of phage virion proteins with a random forest model.Frontiers in genetics · 2022Article
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- Incorporating Machine Learning into Established Bioinformatics Frameworks.International journal of molecular sciences · 2021Review
- SARS-CoV-2-human protein-protein interaction network.Informatics in medicine unlocked · 2020Article
- Machine-learning techniques for the prediction of protein-protein interactions.Journal of biosciences · 2019Article
- Analysis of theInternational journal of molecular sciences · 2019Article
- Article
- A novel matrix of sequence descriptors for predicting protein-protein interactions from amino acid sequences.PloS one · 2019Article
- Review of computational methods for virus-host protein interaction prediction: a case study on novel Ebola-human interactions.Briefings in functional genomics · 2018Review
- Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery.Molecules (Basel, Switzerland) · 2018Review
- Protein-protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM.Journal of biosciences · 2015Article
- FunPred-1: protein function prediction from a protein interaction network using neighborhood analysis.Cellular & molecular biology letters · 2014Article
- PPIcons: identification of protein-protein interaction sites in selected organisms.Journal of molecular modeling · 2013Article
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5 authors.
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Abstract
Protein-protein interactions (PPI) control most of the biological processes in a living cell. In order to fully understand protein functions, a knowledge of protein-protein interactions is necessary. Prediction of PPI is challenging, especially when the three-dimensional structure of interacting partners is not known. Recently, a novel prediction method was proposed by exploiting physical interactions of constituent domains. We propose here a novel knowledge-based prediction method, namely PPI_SVM, which predicts interactions between two protein sequences by exploiting their domain information. We trained a two-class support vector machine on the benchmarking set of pairs of interacting proteins extracted from the Database of Interacting Proteins (DIP). The method considers all possible combinations of constituent domains between two protein sequences, unlike most of the existing approaches. Moreover, it deals with both single-domain proteins and multi domain proteins; therefore it can be applied to the whole proteome in high-throughput studies. Our machine learning classifier, following a brainstorming approach, achieves accuracy of 86%, with specificity of 95%, and sensitivity of 75%, which are better results than most previous methods that sacrifice recall values in order to boost the overall precision. Our method has on average better sensitivity combined with good selectivity on the benchmarking dataset. The PPI_SVM source code, train/test datasets and supplementary files are available freely in the public domain at: http://code.google.com/p/cmater-bioinfo/.
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