Cargando…
Prediction of RNA-Binding Proteins by Voting Systems
It is important to identify which proteins can interact with RNA for the purpose of protein annotation, since interactions between RNA and proteins influence the structure of the ribosome and play important roles in gene expression. This paper tries to identify proteins that can interact with RNA us...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2011
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3149752/ https://www.ncbi.nlm.nih.gov/pubmed/21826121 http://dx.doi.org/10.1155/2011/506205 |
_version_ | 1782209490684215296 |
---|---|
author | Peng, C. R. Liu, L. Niu, B. Lv, Y. L. Li, M. J. Yuan, Y. L. Zhu, Y. B. Lu, W. C. Cai, Y. D. |
author_facet | Peng, C. R. Liu, L. Niu, B. Lv, Y. L. Li, M. J. Yuan, Y. L. Zhu, Y. B. Lu, W. C. Cai, Y. D. |
author_sort | Peng, C. R. |
collection | PubMed |
description | It is important to identify which proteins can interact with RNA for the purpose of protein annotation, since interactions between RNA and proteins influence the structure of the ribosome and play important roles in gene expression. This paper tries to identify proteins that can interact with RNA using voting systems. Firstly through Weka, 34 learning algorithms are chosen for investigation. Then simple majority voting system (SMVS) is used for the prediction of RNA-binding proteins, achieving average ACC (overall prediction accuracy) value of 79.72% and MCC (Matthew's correlation coefficient) value of 59.77% for the independent testing dataset. Then mRMR (minimum redundancy maximum relevance) strategy is used, which is transferred into algorithm selection. In addition, the MCC value of each classifier is assigned to be the weight of the classifier's vote. As a result, best average MCC values are attained when 22 algorithms are selected and integrated through weighted votes, which are 64.70% for the independent testing dataset, and ACC value is 82.04% at this moment. |
format | Online Article Text |
id | pubmed-3149752 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-31497522011-08-08 Prediction of RNA-Binding Proteins by Voting Systems Peng, C. R. Liu, L. Niu, B. Lv, Y. L. Li, M. J. Yuan, Y. L. Zhu, Y. B. Lu, W. C. Cai, Y. D. J Biomed Biotechnol Research Article It is important to identify which proteins can interact with RNA for the purpose of protein annotation, since interactions between RNA and proteins influence the structure of the ribosome and play important roles in gene expression. This paper tries to identify proteins that can interact with RNA using voting systems. Firstly through Weka, 34 learning algorithms are chosen for investigation. Then simple majority voting system (SMVS) is used for the prediction of RNA-binding proteins, achieving average ACC (overall prediction accuracy) value of 79.72% and MCC (Matthew's correlation coefficient) value of 59.77% for the independent testing dataset. Then mRMR (minimum redundancy maximum relevance) strategy is used, which is transferred into algorithm selection. In addition, the MCC value of each classifier is assigned to be the weight of the classifier's vote. As a result, best average MCC values are attained when 22 algorithms are selected and integrated through weighted votes, which are 64.70% for the independent testing dataset, and ACC value is 82.04% at this moment. Hindawi Publishing Corporation 2011 2011-07-26 /pmc/articles/PMC3149752/ /pubmed/21826121 http://dx.doi.org/10.1155/2011/506205 Text en Copyright © 2011 C. R. Peng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Peng, C. R. Liu, L. Niu, B. Lv, Y. L. Li, M. J. Yuan, Y. L. Zhu, Y. B. Lu, W. C. Cai, Y. D. Prediction of RNA-Binding Proteins by Voting Systems |
title | Prediction of RNA-Binding Proteins by Voting Systems |
title_full | Prediction of RNA-Binding Proteins by Voting Systems |
title_fullStr | Prediction of RNA-Binding Proteins by Voting Systems |
title_full_unstemmed | Prediction of RNA-Binding Proteins by Voting Systems |
title_short | Prediction of RNA-Binding Proteins by Voting Systems |
title_sort | prediction of rna-binding proteins by voting systems |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3149752/ https://www.ncbi.nlm.nih.gov/pubmed/21826121 http://dx.doi.org/10.1155/2011/506205 |
work_keys_str_mv | AT pengcr predictionofrnabindingproteinsbyvotingsystems AT liul predictionofrnabindingproteinsbyvotingsystems AT niub predictionofrnabindingproteinsbyvotingsystems AT lvyl predictionofrnabindingproteinsbyvotingsystems AT limj predictionofrnabindingproteinsbyvotingsystems AT yuanyl predictionofrnabindingproteinsbyvotingsystems AT zhuyb predictionofrnabindingproteinsbyvotingsystems AT luwc predictionofrnabindingproteinsbyvotingsystems AT caiyd predictionofrnabindingproteinsbyvotingsystems |