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Accurate classification of membrane protein types based on sequence and evolutionary information using deep learning
BACKGROUND: Membrane proteins play an important role in the life activities of organisms. Knowing membrane protein types provides clues for understanding the structure and function of proteins. Though various computational methods for predicting membrane protein types have been developed, the result...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929490/ https://www.ncbi.nlm.nih.gov/pubmed/31874615 http://dx.doi.org/10.1186/s12859-019-3275-6 |
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author | Guo, Lei Wang, Shunfang Li, Mingyuan Cao, Zicheng |
author_facet | Guo, Lei Wang, Shunfang Li, Mingyuan Cao, Zicheng |
author_sort | Guo, Lei |
collection | PubMed |
description | BACKGROUND: Membrane proteins play an important role in the life activities of organisms. Knowing membrane protein types provides clues for understanding the structure and function of proteins. Though various computational methods for predicting membrane protein types have been developed, the results still do not meet the expectations of researchers. RESULTS: We propose two deep learning models to process sequence information and evolutionary information, respectively. Both models obtained better results than traditional machine learning models. Furthermore, to improve the performance of the sequence information model, we also provide a new vector representation method to replace the one-hot encoding, whose overall success rate improved by 3.81% and 6.55% on two datasets. Finally, a more effective model is obtained by fusing the above two models, whose overall success rate reached 95.68% and 92.98% on two datasets. CONCLUSION: The final experimental results show that our method is more effective than existing methods for predicting membrane protein types, which can help laboratory researchers to identify the type of novel membrane proteins. |
format | Online Article Text |
id | pubmed-6929490 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69294902019-12-30 Accurate classification of membrane protein types based on sequence and evolutionary information using deep learning Guo, Lei Wang, Shunfang Li, Mingyuan Cao, Zicheng BMC Bioinformatics Research BACKGROUND: Membrane proteins play an important role in the life activities of organisms. Knowing membrane protein types provides clues for understanding the structure and function of proteins. Though various computational methods for predicting membrane protein types have been developed, the results still do not meet the expectations of researchers. RESULTS: We propose two deep learning models to process sequence information and evolutionary information, respectively. Both models obtained better results than traditional machine learning models. Furthermore, to improve the performance of the sequence information model, we also provide a new vector representation method to replace the one-hot encoding, whose overall success rate improved by 3.81% and 6.55% on two datasets. Finally, a more effective model is obtained by fusing the above two models, whose overall success rate reached 95.68% and 92.98% on two datasets. CONCLUSION: The final experimental results show that our method is more effective than existing methods for predicting membrane protein types, which can help laboratory researchers to identify the type of novel membrane proteins. BioMed Central 2019-12-24 /pmc/articles/PMC6929490/ /pubmed/31874615 http://dx.doi.org/10.1186/s12859-019-3275-6 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Guo, Lei Wang, Shunfang Li, Mingyuan Cao, Zicheng Accurate classification of membrane protein types based on sequence and evolutionary information using deep learning |
title | Accurate classification of membrane protein types based on sequence and evolutionary information using deep learning |
title_full | Accurate classification of membrane protein types based on sequence and evolutionary information using deep learning |
title_fullStr | Accurate classification of membrane protein types based on sequence and evolutionary information using deep learning |
title_full_unstemmed | Accurate classification of membrane protein types based on sequence and evolutionary information using deep learning |
title_short | Accurate classification of membrane protein types based on sequence and evolutionary information using deep learning |
title_sort | accurate classification of membrane protein types based on sequence and evolutionary information using deep learning |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929490/ https://www.ncbi.nlm.nih.gov/pubmed/31874615 http://dx.doi.org/10.1186/s12859-019-3275-6 |
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