nsemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Predictio

Autor(es)

de Oliveira, Gabriel Bianchin ^[1] ; Pedrini, Helio ^[1] ; Dias, Zanoni ^[1]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas - Brazil

• Tipo de documento: Artigo Científico
• Fonte: INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES; v. 22, n. 21 NOV 2021.
• Citações Web of Science: 0

Resumo

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. (AU)

• Processo FAPESP: 19/20875-8 - Classificação de imagens de radiografias de tórax utilizando redes neurais profundas
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