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Prediction of Protein–Protein Interaction with Pairwise Kernel Support Vector Machine
Protein–protein interactions (PPIs) play a key role in many cellular processes. Unfortunately, the experimental methods currently used to identify PPIs are both time-consuming and expensive. These obstacles could be overcome by developing computational approaches to predict PPIs. Here, we report two...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3958907/ https://www.ncbi.nlm.nih.gov/pubmed/24566145 http://dx.doi.org/10.3390/ijms15023220 |
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author | Zhang, Shao-Wu Hao, Li-Yang Zhang, Ting-He |
author_facet | Zhang, Shao-Wu Hao, Li-Yang Zhang, Ting-He |
author_sort | Zhang, Shao-Wu |
collection | PubMed |
description | Protein–protein interactions (PPIs) play a key role in many cellular processes. Unfortunately, the experimental methods currently used to identify PPIs are both time-consuming and expensive. These obstacles could be overcome by developing computational approaches to predict PPIs. Here, we report two methods of amino acids feature extraction: (i) distance frequency with PCA reducing the dimension (DFPCA) and (ii) amino acid index distribution (AAID) representing the protein sequences. In order to obtain the most robust and reliable results for PPI prediction, pairwise kernel function and support vector machines (SVM) were employed to avoid the concatenation order of two feature vectors generated with two proteins. The highest prediction accuracies of AAID and DFPCA were 94% and 93.96%, respectively, using the 10 CV test, and the results of pairwise radial basis kernel function are considerably improved over those based on radial basis kernel function. Overall, the PPI prediction tool, termed PPI-PKSVM, which is freely available at http://159.226.118.31/PPI/index.html, promises to become useful in such areas as bio-analysis and drug development. |
format | Online Article Text |
id | pubmed-3958907 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-39589072014-03-20 Prediction of Protein–Protein Interaction with Pairwise Kernel Support Vector Machine Zhang, Shao-Wu Hao, Li-Yang Zhang, Ting-He Int J Mol Sci Article Protein–protein interactions (PPIs) play a key role in many cellular processes. Unfortunately, the experimental methods currently used to identify PPIs are both time-consuming and expensive. These obstacles could be overcome by developing computational approaches to predict PPIs. Here, we report two methods of amino acids feature extraction: (i) distance frequency with PCA reducing the dimension (DFPCA) and (ii) amino acid index distribution (AAID) representing the protein sequences. In order to obtain the most robust and reliable results for PPI prediction, pairwise kernel function and support vector machines (SVM) were employed to avoid the concatenation order of two feature vectors generated with two proteins. The highest prediction accuracies of AAID and DFPCA were 94% and 93.96%, respectively, using the 10 CV test, and the results of pairwise radial basis kernel function are considerably improved over those based on radial basis kernel function. Overall, the PPI prediction tool, termed PPI-PKSVM, which is freely available at http://159.226.118.31/PPI/index.html, promises to become useful in such areas as bio-analysis and drug development. Molecular Diversity Preservation International (MDPI) 2014-02-21 /pmc/articles/PMC3958907/ /pubmed/24566145 http://dx.doi.org/10.3390/ijms15023220 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland http://creativecommons.org/licenses/by/3.0/ This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Zhang, Shao-Wu Hao, Li-Yang Zhang, Ting-He Prediction of Protein–Protein Interaction with Pairwise Kernel Support Vector Machine |
title | Prediction of Protein–Protein Interaction with Pairwise Kernel Support Vector Machine |
title_full | Prediction of Protein–Protein Interaction with Pairwise Kernel Support Vector Machine |
title_fullStr | Prediction of Protein–Protein Interaction with Pairwise Kernel Support Vector Machine |
title_full_unstemmed | Prediction of Protein–Protein Interaction with Pairwise Kernel Support Vector Machine |
title_short | Prediction of Protein–Protein Interaction with Pairwise Kernel Support Vector Machine |
title_sort | prediction of protein–protein interaction with pairwise kernel support vector machine |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3958907/ https://www.ncbi.nlm.nih.gov/pubmed/24566145 http://dx.doi.org/10.3390/ijms15023220 |
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