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Identification of Antioxidant Proteins With Deep Learning From Sequence Information
Antioxidant proteins have been found closely linked to disease control for its ability to eliminate excess free radicals. Because of its medicinal value, the study of identifying antioxidant proteins is on the upsurge. Many machine-learning classifiers have performed poorly owing to the nonlinear an...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158654/ https://www.ncbi.nlm.nih.gov/pubmed/30294271 http://dx.doi.org/10.3389/fphar.2018.01036 |
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author | Shao, Lifen Gao, Hui Liu, Zhen Feng, Juan Tang, Lixia Lin, Hao |
author_facet | Shao, Lifen Gao, Hui Liu, Zhen Feng, Juan Tang, Lixia Lin, Hao |
author_sort | Shao, Lifen |
collection | PubMed |
description | Antioxidant proteins have been found closely linked to disease control for its ability to eliminate excess free radicals. Because of its medicinal value, the study of identifying antioxidant proteins is on the upsurge. Many machine-learning classifiers have performed poorly owing to the nonlinear and unbalanced nature of biological data. Recently, deep learning techniques showed advantages over many state-of-the-art machine learning methods in various fields. In this study, a deep learning based classifier was proposed to identify antioxidant proteins based on mixed g-gap dipeptide composition feature vector. The classifier employed deep autoencoder to extract nonlinear representation from raw input. The t-Distributed Stochastic Neighbor Embedding (t-SNE) was used for dimensionality reduction. Support vector machine was finally performed for classification. The classifier achieved F(1) score of 0.8842 and MCC of 0.7409 in 10-fold cross validation. Experimental results show that our proposed method outperformed the traditional machine learning methods and could be a promising tool for antioxidant protein identification. For the convenience of others' scientific research, we have developed a user-friendly web server called IDAod for antioxidant protein identification, which can be accessed freely at http://bigroup.uestc.edu.cn/IDAod/. |
format | Online Article Text |
id | pubmed-6158654 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-61586542018-10-05 Identification of Antioxidant Proteins With Deep Learning From Sequence Information Shao, Lifen Gao, Hui Liu, Zhen Feng, Juan Tang, Lixia Lin, Hao Front Pharmacol Pharmacology Antioxidant proteins have been found closely linked to disease control for its ability to eliminate excess free radicals. Because of its medicinal value, the study of identifying antioxidant proteins is on the upsurge. Many machine-learning classifiers have performed poorly owing to the nonlinear and unbalanced nature of biological data. Recently, deep learning techniques showed advantages over many state-of-the-art machine learning methods in various fields. In this study, a deep learning based classifier was proposed to identify antioxidant proteins based on mixed g-gap dipeptide composition feature vector. The classifier employed deep autoencoder to extract nonlinear representation from raw input. The t-Distributed Stochastic Neighbor Embedding (t-SNE) was used for dimensionality reduction. Support vector machine was finally performed for classification. The classifier achieved F(1) score of 0.8842 and MCC of 0.7409 in 10-fold cross validation. Experimental results show that our proposed method outperformed the traditional machine learning methods and could be a promising tool for antioxidant protein identification. For the convenience of others' scientific research, we have developed a user-friendly web server called IDAod for antioxidant protein identification, which can be accessed freely at http://bigroup.uestc.edu.cn/IDAod/. Frontiers Media S.A. 2018-09-20 /pmc/articles/PMC6158654/ /pubmed/30294271 http://dx.doi.org/10.3389/fphar.2018.01036 Text en Copyright © 2018 Shao, Gao, Liu, Feng, Tang and Lin. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pharmacology Shao, Lifen Gao, Hui Liu, Zhen Feng, Juan Tang, Lixia Lin, Hao Identification of Antioxidant Proteins With Deep Learning From Sequence Information |
title | Identification of Antioxidant Proteins With Deep Learning From Sequence Information |
title_full | Identification of Antioxidant Proteins With Deep Learning From Sequence Information |
title_fullStr | Identification of Antioxidant Proteins With Deep Learning From Sequence Information |
title_full_unstemmed | Identification of Antioxidant Proteins With Deep Learning From Sequence Information |
title_short | Identification of Antioxidant Proteins With Deep Learning From Sequence Information |
title_sort | identification of antioxidant proteins with deep learning from sequence information |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158654/ https://www.ncbi.nlm.nih.gov/pubmed/30294271 http://dx.doi.org/10.3389/fphar.2018.01036 |
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