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Int. J. Mol. Sci. 2016, 17(12), 2118; doi:10.3390/ijms17122118

Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition

School of Computer Science and Technology, Tianjin University, Tianjin 300354, China
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Academic Editor: Salvador Ventura
Received: 16 November 2016 / Revised: 3 December 2016 / Accepted: 11 December 2016 / Published: 16 December 2016
(This article belongs to the Collection Protein Folding)
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Abstract

Knowledge on protein folding has a profound impact on understanding the heterogeneity and molecular function of proteins, further facilitating drug design. Predicting the 3D structure (fold) of a protein is a key problem in molecular biology. Determination of the fold of a protein mainly relies on molecular experimental methods. With the development of next-generation sequencing techniques, the discovery of new protein sequences has been rapidly increasing. With such a great number of proteins, the use of experimental techniques to determine protein folding is extremely difficult because these techniques are time consuming and expensive. Thus, developing computational prediction methods that can automatically, rapidly, and accurately classify unknown protein sequences into specific fold categories is urgently needed. Computational recognition of protein folds has been a recent research hotspot in bioinformatics and computational biology. Many computational efforts have been made, generating a variety of computational prediction methods. In this review, we conduct a comprehensive survey of recent computational methods, especially machine learning-based methods, for protein fold recognition. This review is anticipated to assist researchers in their pursuit to systematically understand the computational recognition of protein folds. View Full-Text
Keywords: protein fold recognition; machine learning; computational method protein fold recognition; machine learning; computational method
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Wei, L.; Zou, Q. Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition. Int. J. Mol. Sci. 2016, 17, 2118.

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