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A Novel Method of Predicting Protein Disordered Regions Based on Sequence Features
With a large number of disordered proteins and their important functions discovered, it is highly desired to develop effective methods to computationally predict protein disordered regions. In this study, based on Random Forest (RF), Maximum Relevancy Minimum Redundancy (mRMR), and Incremental Featu...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3654632/ https://www.ncbi.nlm.nih.gov/pubmed/23710446 http://dx.doi.org/10.1155/2013/414327 |
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author | Zhao, Tong-Hui Jiang, Min Huang, Tao Li, Bi-Qing Zhang, Ning Li, Hai-Peng Cai, Yu-Dong |
author_facet | Zhao, Tong-Hui Jiang, Min Huang, Tao Li, Bi-Qing Zhang, Ning Li, Hai-Peng Cai, Yu-Dong |
author_sort | Zhao, Tong-Hui |
collection | PubMed |
description | With a large number of disordered proteins and their important functions discovered, it is highly desired to develop effective methods to computationally predict protein disordered regions. In this study, based on Random Forest (RF), Maximum Relevancy Minimum Redundancy (mRMR), and Incremental Feature Selection (IFS), we developed a new method to predict disordered regions in proteins. The mRMR criterion was used to rank the importance of all candidate features. Finally, top 128 features were selected from the ranked feature list to build the optimal model, including 92 Position Specific Scoring Matrix (PSSM) conservation score features and 36 secondary structure features. As a result, Matthews correlation coefficient (MCC) of 0.3895 was achieved on the training set by 10-fold cross-validation. On the basis of predicting results for each query sequence by using the method, we used the scanning and modification strategy to improve the performance. The accuracy (ACC) and MCC were increased by 4% and almost 0.2%, respectively, compared with other three popular predictors: DISOPRED, DISOclust, and OnD-CRF. The selected features may shed some light on the understanding of the formation mechanism of disordered structures, providing guidelines for experimental validation. |
format | Online Article Text |
id | pubmed-3654632 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-36546322013-05-24 A Novel Method of Predicting Protein Disordered Regions Based on Sequence Features Zhao, Tong-Hui Jiang, Min Huang, Tao Li, Bi-Qing Zhang, Ning Li, Hai-Peng Cai, Yu-Dong Biomed Res Int Research Article With a large number of disordered proteins and their important functions discovered, it is highly desired to develop effective methods to computationally predict protein disordered regions. In this study, based on Random Forest (RF), Maximum Relevancy Minimum Redundancy (mRMR), and Incremental Feature Selection (IFS), we developed a new method to predict disordered regions in proteins. The mRMR criterion was used to rank the importance of all candidate features. Finally, top 128 features were selected from the ranked feature list to build the optimal model, including 92 Position Specific Scoring Matrix (PSSM) conservation score features and 36 secondary structure features. As a result, Matthews correlation coefficient (MCC) of 0.3895 was achieved on the training set by 10-fold cross-validation. On the basis of predicting results for each query sequence by using the method, we used the scanning and modification strategy to improve the performance. The accuracy (ACC) and MCC were increased by 4% and almost 0.2%, respectively, compared with other three popular predictors: DISOPRED, DISOclust, and OnD-CRF. The selected features may shed some light on the understanding of the formation mechanism of disordered structures, providing guidelines for experimental validation. Hindawi Publishing Corporation 2013 2013-04-22 /pmc/articles/PMC3654632/ /pubmed/23710446 http://dx.doi.org/10.1155/2013/414327 Text en Copyright © 2013 Tong-Hui Zhao et al. https://creativecommons.org/licenses/by/3.0/ 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 Zhao, Tong-Hui Jiang, Min Huang, Tao Li, Bi-Qing Zhang, Ning Li, Hai-Peng Cai, Yu-Dong A Novel Method of Predicting Protein Disordered Regions Based on Sequence Features |
title | A Novel Method of Predicting Protein Disordered Regions Based on Sequence Features |
title_full | A Novel Method of Predicting Protein Disordered Regions Based on Sequence Features |
title_fullStr | A Novel Method of Predicting Protein Disordered Regions Based on Sequence Features |
title_full_unstemmed | A Novel Method of Predicting Protein Disordered Regions Based on Sequence Features |
title_short | A Novel Method of Predicting Protein Disordered Regions Based on Sequence Features |
title_sort | novel method of predicting protein disordered regions based on sequence features |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3654632/ https://www.ncbi.nlm.nih.gov/pubmed/23710446 http://dx.doi.org/10.1155/2013/414327 |
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