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Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM

Protein-protein interactions (PPIs) play a very large part in most cellular processes. Although a great deal of research has been devoted to detecting PPIs through high-throughput technologies, these methods are clearly expensive and cumbersome. Compared with the traditional experimental methods, co...

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Detalles Bibliográficos
Autores principales: Wang, Yan-Bin, You, Zhu-Hong, Li, Li-Ping, Huang, Yu-An, Yi, Hai-Cheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6152086/
https://www.ncbi.nlm.nih.gov/pubmed/28820478
http://dx.doi.org/10.3390/molecules22081366
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author Wang, Yan-Bin
You, Zhu-Hong
Li, Li-Ping
Huang, Yu-An
Yi, Hai-Cheng
author_facet Wang, Yan-Bin
You, Zhu-Hong
Li, Li-Ping
Huang, Yu-An
Yi, Hai-Cheng
author_sort Wang, Yan-Bin
collection PubMed
description Protein-protein interactions (PPIs) play a very large part in most cellular processes. Although a great deal of research has been devoted to detecting PPIs through high-throughput technologies, these methods are clearly expensive and cumbersome. Compared with the traditional experimental methods, computational methods have attracted much attention because of their good performance in detecting PPIs. In our work, a novel computational method named as PCVM-LM is proposed which combines the probabilistic classification vector machine (PCVM) model and Legendre moments (LMs) to predict PPIs from amino acid sequences. The improvement mainly comes from using the LMs to extract discriminatory information embedded in the position-specific scoring matrix (PSSM) combined with the PCVM classifier to implement prediction. The proposed method was evaluated on Yeast and Helicobacter pylori datasets with five-fold cross-validation experiments. The experimental results show that the proposed method achieves high average accuracies of 96.37% and 93.48%, respectively, which are much better than other well-known methods. To further evaluate the proposed method, we also compared the proposed method with the state-of-the-art support vector machine (SVM) classifier and other existing methods on the same datasets. The comparison results clearly show that our method is better than the SVM-based method and other existing methods. The promising experimental results show the reliability and effectiveness of the proposed method, which can be a useful decision support tool for protein research.
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spelling pubmed-61520862018-11-13 Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM Wang, Yan-Bin You, Zhu-Hong Li, Li-Ping Huang, Yu-An Yi, Hai-Cheng Molecules Article Protein-protein interactions (PPIs) play a very large part in most cellular processes. Although a great deal of research has been devoted to detecting PPIs through high-throughput technologies, these methods are clearly expensive and cumbersome. Compared with the traditional experimental methods, computational methods have attracted much attention because of their good performance in detecting PPIs. In our work, a novel computational method named as PCVM-LM is proposed which combines the probabilistic classification vector machine (PCVM) model and Legendre moments (LMs) to predict PPIs from amino acid sequences. The improvement mainly comes from using the LMs to extract discriminatory information embedded in the position-specific scoring matrix (PSSM) combined with the PCVM classifier to implement prediction. The proposed method was evaluated on Yeast and Helicobacter pylori datasets with five-fold cross-validation experiments. The experimental results show that the proposed method achieves high average accuracies of 96.37% and 93.48%, respectively, which are much better than other well-known methods. To further evaluate the proposed method, we also compared the proposed method with the state-of-the-art support vector machine (SVM) classifier and other existing methods on the same datasets. The comparison results clearly show that our method is better than the SVM-based method and other existing methods. The promising experimental results show the reliability and effectiveness of the proposed method, which can be a useful decision support tool for protein research. MDPI 2017-08-18 /pmc/articles/PMC6152086/ /pubmed/28820478 http://dx.doi.org/10.3390/molecules22081366 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Yan-Bin
You, Zhu-Hong
Li, Li-Ping
Huang, Yu-An
Yi, Hai-Cheng
Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM
title Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM
title_full Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM
title_fullStr Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM
title_full_unstemmed Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM
title_short Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM
title_sort detection of interactions between proteins by using legendre moments descriptor to extract discriminatory information embedded in pssm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6152086/
https://www.ncbi.nlm.nih.gov/pubmed/28820478
http://dx.doi.org/10.3390/molecules22081366
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