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Prediction of Aptamer-Target Interacting Pairs with Pseudo-Amino Acid Composition

Aptamers are oligonucleic acid or peptide molecules that bind to specific target molecules. As a novel and powerful class of ligands, aptamers are thought to have excellent potential for applications in the fields of biosensing, diagnostics and therapeutics. In this study, a new method for predictin...

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Detalles Bibliográficos
Autores principales: Li, Bi-Qing, Zhang, Yu-Chao, Huang, Guo-Hua, Cui, Wei-Ren, Zhang, Ning, Cai, Yu-Dong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3899287/
https://www.ncbi.nlm.nih.gov/pubmed/24466214
http://dx.doi.org/10.1371/journal.pone.0086729
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author Li, Bi-Qing
Zhang, Yu-Chao
Huang, Guo-Hua
Cui, Wei-Ren
Zhang, Ning
Cai, Yu-Dong
author_facet Li, Bi-Qing
Zhang, Yu-Chao
Huang, Guo-Hua
Cui, Wei-Ren
Zhang, Ning
Cai, Yu-Dong
author_sort Li, Bi-Qing
collection PubMed
description Aptamers are oligonucleic acid or peptide molecules that bind to specific target molecules. As a novel and powerful class of ligands, aptamers are thought to have excellent potential for applications in the fields of biosensing, diagnostics and therapeutics. In this study, a new method for predicting aptamer-target interacting pairs was proposed by integrating features derived from both aptamers and their targets. Features of nucleotide composition and traditional amino acid composition as well as pseudo amino acid were utilized to represent aptamers and targets, respectively. The predictor was constructed based on Random Forest and the optimal features were selected by using the maximum relevance minimum redundancy (mRMR) method and the incremental feature selection (IFS) method. As a result, 81.34% accuracy and 0.4612 MCC were obtained for the training dataset, and 77.41% accuracy and 0.3717 MCC were achieved for the testing dataset. An optimal feature set of 220 features were selected, which were considered as the ones that contributed significantly to the interacting aptamer-target pair predictions. Analysis of the optimal feature set indicated several important factors in determining aptamer-target interactions. It is anticipated that our prediction method may become a useful tool for identifying aptamer-target pairs and the features selected and analyzed in this study may provide useful insights into the mechanism of interactions between aptamers and targets.
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spelling pubmed-38992872014-01-24 Prediction of Aptamer-Target Interacting Pairs with Pseudo-Amino Acid Composition Li, Bi-Qing Zhang, Yu-Chao Huang, Guo-Hua Cui, Wei-Ren Zhang, Ning Cai, Yu-Dong PLoS One Research Article Aptamers are oligonucleic acid or peptide molecules that bind to specific target molecules. As a novel and powerful class of ligands, aptamers are thought to have excellent potential for applications in the fields of biosensing, diagnostics and therapeutics. In this study, a new method for predicting aptamer-target interacting pairs was proposed by integrating features derived from both aptamers and their targets. Features of nucleotide composition and traditional amino acid composition as well as pseudo amino acid were utilized to represent aptamers and targets, respectively. The predictor was constructed based on Random Forest and the optimal features were selected by using the maximum relevance minimum redundancy (mRMR) method and the incremental feature selection (IFS) method. As a result, 81.34% accuracy and 0.4612 MCC were obtained for the training dataset, and 77.41% accuracy and 0.3717 MCC were achieved for the testing dataset. An optimal feature set of 220 features were selected, which were considered as the ones that contributed significantly to the interacting aptamer-target pair predictions. Analysis of the optimal feature set indicated several important factors in determining aptamer-target interactions. It is anticipated that our prediction method may become a useful tool for identifying aptamer-target pairs and the features selected and analyzed in this study may provide useful insights into the mechanism of interactions between aptamers and targets. Public Library of Science 2014-01-22 /pmc/articles/PMC3899287/ /pubmed/24466214 http://dx.doi.org/10.1371/journal.pone.0086729 Text en © 2014 Li et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Li, Bi-Qing
Zhang, Yu-Chao
Huang, Guo-Hua
Cui, Wei-Ren
Zhang, Ning
Cai, Yu-Dong
Prediction of Aptamer-Target Interacting Pairs with Pseudo-Amino Acid Composition
title Prediction of Aptamer-Target Interacting Pairs with Pseudo-Amino Acid Composition
title_full Prediction of Aptamer-Target Interacting Pairs with Pseudo-Amino Acid Composition
title_fullStr Prediction of Aptamer-Target Interacting Pairs with Pseudo-Amino Acid Composition
title_full_unstemmed Prediction of Aptamer-Target Interacting Pairs with Pseudo-Amino Acid Composition
title_short Prediction of Aptamer-Target Interacting Pairs with Pseudo-Amino Acid Composition
title_sort prediction of aptamer-target interacting pairs with pseudo-amino acid composition
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3899287/
https://www.ncbi.nlm.nih.gov/pubmed/24466214
http://dx.doi.org/10.1371/journal.pone.0086729
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