Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction

Protein secondary structures are important in many biological processes and applications. Due to advances in sequencing methods, there are many proteins sequenced, but fewer proteins with secondary structures defined by laboratory methods. With the development of computer technology, computational m...

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Autores principales:	Gabriel Bianchin de Oliveira, Helio Pedrini, Zanoni Dias
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	protein secondary structure prediction deep learning machine learning BLAST ensemble Biology (General) QH301-705.5 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/f8d917ae423c40feba70fee97ddf59be
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spelling	oai:doaj.org-article:f8d917ae423c40feba70fee97ddf59be2021-11-11T16:54:43ZEnsemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction10.3390/ijms2221114491422-00671661-6596https://doaj.org/article/f8d917ae423c40feba70fee97ddf59be2021-10-01T00:00:00Zhttps://www.mdpi.com/1422-0067/22/21/11449https://doaj.org/toc/1661-6596https://doaj.org/toc/1422-0067Protein secondary structures are important in many biological processes and applications. Due to advances in sequencing methods, there are many proteins sequenced, but fewer proteins with secondary structures defined by laboratory methods. With the development of computer technology, computational methods have (started to) become the most important methodologies for predicting secondary structures. We evaluated two different approaches to this problem—driven by the recent results obtained by computational methods in this task—(i) template-free classifiers, based on machine learning techniques; and (ii) template-based classifiers, based on searching tools. Both approaches are formed by different sub-classifiers—six for template-free and two for template-based, each with a specific view of the protein. Our results show that these ensembles improve the results of each approach individually.Gabriel Bianchin de OliveiraHelio PedriniZanoni DiasMDPI AGarticleprotein secondary structure predictiondeep learningmachine learningBLASTensembleBiology (General)QH301-705.5ChemistryQD1-999ENInternational Journal of Molecular Sciences, Vol 22, Iss 11449, p 11449 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	protein secondary structure prediction deep learning machine learning BLAST ensemble Biology (General) QH301-705.5 Chemistry QD1-999
spellingShingle	protein secondary structure prediction deep learning machine learning BLAST ensemble Biology (General) QH301-705.5 Chemistry QD1-999 Gabriel Bianchin de Oliveira Helio Pedrini Zanoni Dias Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction
description	Protein secondary structures are important in many biological processes and applications. Due to advances in sequencing methods, there are many proteins sequenced, but fewer proteins with secondary structures defined by laboratory methods. With the development of computer technology, computational methods have (started to) become the most important methodologies for predicting secondary structures. We evaluated two different approaches to this problem—driven by the recent results obtained by computational methods in this task—(i) template-free classifiers, based on machine learning techniques; and (ii) template-based classifiers, based on searching tools. Both approaches are formed by different sub-classifiers—six for template-free and two for template-based, each with a specific view of the protein. Our results show that these ensembles improve the results of each approach individually.
format	article
author	Gabriel Bianchin de Oliveira Helio Pedrini Zanoni Dias
author_facet	Gabriel Bianchin de Oliveira Helio Pedrini Zanoni Dias
author_sort	Gabriel Bianchin de Oliveira
title	Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction
title_short	Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction
title_full	Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction
title_fullStr	Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction
title_full_unstemmed	Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction
title_sort	ensemble of template-free and template-based classifiers for protein secondary structure prediction
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f8d917ae423c40feba70fee97ddf59be
work_keys_str_mv	AT gabrielbianchindeoliveira ensembleoftemplatefreeandtemplatebasedclassifiersforproteinsecondarystructureprediction AT heliopedrini ensembleoftemplatefreeandtemplatebasedclassifiersforproteinsecondarystructureprediction AT zanonidias ensembleoftemplatefreeandtemplatebasedclassifiersforproteinsecondarystructureprediction
_version_	1718432216340496384

Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction

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