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Article

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

Institute of Computing, University of Campinas, Campinas 13083-852, Brazil
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Author to whom correspondence should be addressed.
Academic Editor: Christo Z. Christov
Int. J. Mol. Sci. 2021, 22(21), 11449; https://doi.org/10.3390/ijms222111449
Received: 14 September 2021 / Revised: 18 October 2021 / Accepted: 20 October 2021 / Published: 23 October 2021
(This article belongs to the Collection Computational Studies of Biomolecules)
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. View Full-Text
Keywords: protein secondary structure prediction; deep learning; machine learning; BLAST; ensemble protein secondary structure prediction; deep learning; machine learning; BLAST; ensemble
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MDPI and ACS Style

de Oliveira, G.B.; Pedrini, H.; Dias, Z. Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction. Int. J. Mol. Sci. 2021, 22, 11449. https://doi.org/10.3390/ijms222111449

AMA Style

de Oliveira GB, Pedrini H, Dias Z. Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction. International Journal of Molecular Sciences. 2021; 22(21):11449. https://doi.org/10.3390/ijms222111449

Chicago/Turabian Style

de Oliveira, Gabriel B., Helio Pedrini, and Zanoni Dias. 2021. "Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction" International Journal of Molecular Sciences 22, no. 21: 11449. https://doi.org/10.3390/ijms222111449

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