ArticleInternational journal of molecular sciences2021
Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Protein structure prediction via deep learning: an in-depth review.Frontiers in pharmacology · 2025Review
- TEMPROT: protein function annotation using transformers embeddings and homology search.BMC bioinformatics · 2023Article
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3 authors.
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Abstract
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.
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