ArticleBioMed research international2016
Protein Remote Homology Detection Based on an Ensemble Learning Approach.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Ubigo-X: Protein ubiquitination site prediction using ensemble learning with image-based feature representation and weighted voting.Computational and structural biotechnology journal · 2025Article
- Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.International journal of molecular sciences · 2022Review
- Remote homology clustering identifies lowly conserved families of effector proteins in plant-pathogenic fungi.Microbial genomics · 2021Article
- EnACP: An Ensemble Learning Model for Identification of Anticancer Peptides.Frontiers in genetics · 2020Article
- Graph Theory-Based Sequence Descriptors as Remote Homology Predictors.Biomolecules · 2019Article
- Characterization and Prediction of Protein Flexibility Based on Structural Alphabets.BioMed research internationalArticle
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Authors and funding
3 authors.
Funding
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Abstract
Protein remote homology detection is one of the central problems in bioinformatics. Although some computational methods have been proposed, the problem is still far from being solved. In this paper, an ensemble classifier for protein remote homology detection, called SVM-Ensemble, was proposed with a weighted voting strategy. SVM-Ensemble combined three basic classifiers based on different feature spaces, including Kmer, ACC, and SC-PseAAC. These features consider the characteristics of proteins from various perspectives, incorporating both the sequence composition and the sequence-order information along the protein sequences. Experimental results on a widely used benchmark dataset showed that the proposed SVM-Ensemble can obviously improve the predictive performance for the protein remote homology detection. Moreover, it achieved the best performance and outperformed other state-of-the-art methods.
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Registered trials
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