PFP-RFSM: Protein fold prediction by using random forests and sequence motifs

Journal of Biomedical Science and Engineering

ISSN Print: 1937-6871
ISSN Online: 1937-688X
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"PFP-RFSM: Protein fold prediction by using random forests and sequence motifs"
written by Junfei Li, Jigang Wu, Ke Chen,
published by Journal of Biomedical Science and Engineering, Vol.6 No.12, 2013

has been cited by the following article(s):

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[7]	Recent Trends in Machine Learning-based Protein Fold Recognition Methods
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[8]	Machine Learning Methods for the Protein Fold Recognition Problem
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[9]	A Mathematical Model Quantifying Sequence Alignment for Constructing Phylogenetic Trees and Ant-Minor Protein Structure Classification.
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[10]	Relevance of Machine Learning Techniques and Various Protein Features in Protein Fold Classification: A Review
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[11]	Highly Accurate Fragment Library for Protein Fold Recognition
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[12]	An Overview on Protein Fold Classification via Machine Learning Approach
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[13]	ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier
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[14]	Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition
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[15]	Enhanced Feature Extraction from Evolutionary Profiles for Protein Fold Recognition
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[16]	The recognition of multi-class protein folds by adding average chemical shifts of secondary structure elements
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[17]	Recognition of 27-Class Protein Folds by Adding the Interaction of Segments and Motif Information
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[1]	Identification of antioxidant proteins using a discriminative intelligent model of k-space amino acid pairs based descriptors incorporating with ensemble feature selection Biocybernetics and Biomedical Engineering, 2022 DOI:10.1016/j.bbe.2020.10.003

[2]	AFP-CMBPred: Computational identification of antifreeze proteins by extending consensus sequences into multi-blocks evolutionary information Computers in Biology and Medicine, 2021 DOI:10.1016/j.compbiomed.2021.105006

[3]	AFP-CMBPred: Computational identification of antifreeze proteins by extending consensus sequences into multi-blocks evolutionary information Computers in Biology and Medicine, 2021 DOI:10.1016/j.compbiomed.2021.105006

[4]	Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms Crystals, 2021 DOI:10.3390/cryst11040324

[5]	DeepFrag-k: a fragment-based deep learning approach for protein fold recognition BMC Bioinformatics, 2020 DOI:10.1186/s12859-020-3504-z

[6]	Machine Learning Paradigms Intelligent Systems Reference Library, 2019 DOI:10.1007/978-3-319-94030-4_5

[7]	Relevance of Machine Learning Techniques and Various Protein Features in Protein Fold Classification: A Review Current Bioinformatics, 2019 DOI:10.2174/1574893614666190204154038

[8]	ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier BioMed Research International, 2016 DOI:10.1155/2016/6802832

[9]	Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition International Journal of Molecular Sciences, 2016 DOI:10.3390/ijms17122118

[10]	The recognition of multi-class protein folds by adding average chemical shifts of secondary structure elements Saudi Journal of Biological Sciences, 2016 DOI:10.1016/j.sjbs.2015.10.008

[11]	Recognition of 27-Class Protein Folds by Adding the Interaction of Segments and Motif Information BioMed Research International, 2014 DOI:10.1155/2014/262850

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