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  • Protein secondary structure prediction using neural networks and deep learning: A review

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    Sharma247650Accepted.pdf (603.7Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Wardah, Wafaa
    Khan, MGM
    Sharma, Alok
    Rashid, Mahmood A
    Griffith University Author(s)
    Rashid, Mahmood A.
    Sharma, Alok
    Year published
    2019
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    Abstract
    Literature contains over fifty years of accumulated methods proposed by researchers for predicting the secondary structures of proteins in silico. A large part of this collection is comprised of artificial neural network-based approaches, a field of artificial intelligence and machine learning that is gaining increasing popularity in various application areas. The primary objective of this paper is to put together the summary of works that are important but sparse in time, to help new researchers have a clear view of the domain in a single place. An informative introduction to protein secondary structure and artificial neural ...
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    Literature contains over fifty years of accumulated methods proposed by researchers for predicting the secondary structures of proteins in silico. A large part of this collection is comprised of artificial neural network-based approaches, a field of artificial intelligence and machine learning that is gaining increasing popularity in various application areas. The primary objective of this paper is to put together the summary of works that are important but sparse in time, to help new researchers have a clear view of the domain in a single place. An informative introduction to protein secondary structure and artificial neural networks is also included for context. This review will be valuable in designing future methods to improve protein secondary structure prediction accuracy. The various neural network methods found in this problem domain employ varying architectures and feature spaces, and a handful stand out due to significant improvements in prediction. Neural networks with larger feature scope and higher architecture complexity have been found to produce better protein secondary structure prediction. The current prediction accuracy lies around the 84% marks, leaving much room for further improvement in the prediction of secondary structures in silico. It was found that the estimated limit of 88% prediction accuracy has not been reached yet, hence further research is a timely demand.
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    Journal Title
    Computational Biology and Chemistry
    Volume
    81
    DOI
    https://doi.org/10.1016/j.compbiolchem.2019.107093
    Copyright Statement
    © 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Chemical sciences
    Biological sciences
    Science & Technology
    Life Sciences & Biomedicine
    Technology
    Biology
    Computer Science, Interdisciplinary Applications
    Publication URI
    http://hdl.handle.net/10072/391214
    Collection
    • Journal articles

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