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Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach
The prediction of apoptosis protein subcellular localization plays an important role in understanding the progress in cell proliferation and death. Recently computational approaches to this issue have become very popular, since the traditional biological experiments are so costly and time-consuming...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374881/ https://www.ncbi.nlm.nih.gov/pubmed/30834257 http://dx.doi.org/10.1155/2019/2436924 |
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author | Chen, Xingjian Hu, Xuejiao Yi, Wenxin Zou, Xiang Xue, Wei |
author_facet | Chen, Xingjian Hu, Xuejiao Yi, Wenxin Zou, Xiang Xue, Wei |
author_sort | Chen, Xingjian |
collection | PubMed |
description | The prediction of apoptosis protein subcellular localization plays an important role in understanding the progress in cell proliferation and death. Recently computational approaches to this issue have become very popular, since the traditional biological experiments are so costly and time-consuming that they cannot catch up with the growth rate of sequence data anymore. In order to improve the prediction accuracy of apoptosis protein subcellular localization, we proposed a sparse coding method combined with traditional feature extraction algorithm to complete the sparse representation of apoptosis protein sequences, using multilayer pooling based on different sizes of dictionaries to integrate the processed features, as well as oversampling approach to decrease the influences caused by unbalanced data sets. Then the extracted features were input to a support vector machine to predict the subcellular localization of the apoptosis protein. The experiment results obtained by Jackknife test on two benchmark data sets indicate that our method can significantly improve the accuracy of the apoptosis protein subcellular localization prediction. |
format | Online Article Text |
id | pubmed-6374881 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-63748812019-03-04 Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach Chen, Xingjian Hu, Xuejiao Yi, Wenxin Zou, Xiang Xue, Wei Biomed Res Int Research Article The prediction of apoptosis protein subcellular localization plays an important role in understanding the progress in cell proliferation and death. Recently computational approaches to this issue have become very popular, since the traditional biological experiments are so costly and time-consuming that they cannot catch up with the growth rate of sequence data anymore. In order to improve the prediction accuracy of apoptosis protein subcellular localization, we proposed a sparse coding method combined with traditional feature extraction algorithm to complete the sparse representation of apoptosis protein sequences, using multilayer pooling based on different sizes of dictionaries to integrate the processed features, as well as oversampling approach to decrease the influences caused by unbalanced data sets. Then the extracted features were input to a support vector machine to predict the subcellular localization of the apoptosis protein. The experiment results obtained by Jackknife test on two benchmark data sets indicate that our method can significantly improve the accuracy of the apoptosis protein subcellular localization prediction. Hindawi 2019-01-30 /pmc/articles/PMC6374881/ /pubmed/30834257 http://dx.doi.org/10.1155/2019/2436924 Text en Copyright © 2019 Xingjian Chen et al. https://creativecommons.org/licenses/by/4.0/ 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 Chen, Xingjian Hu, Xuejiao Yi, Wenxin Zou, Xiang Xue, Wei Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach |
title | Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach |
title_full | Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach |
title_fullStr | Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach |
title_full_unstemmed | Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach |
title_short | Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach |
title_sort | prediction of apoptosis protein subcellular localization with multilayer sparse coding and oversampling approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374881/ https://www.ncbi.nlm.nih.gov/pubmed/30834257 http://dx.doi.org/10.1155/2019/2436924 |
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