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  • SPOT-1D2: Improving Protein Secondary Structure Prediction using High Sequence Identity Training Set and an Ensemble of Recurrent and Residual-convolutional Neural Networks

    Author(s)
    Singh, Jaspreet
    Singh, Jaswinder
    Paliwal, Kuldip
    Busch, Andrew
    Zhou, Yaoqi
    Griffith University Author(s)
    Paliwal, Kuldip K.
    Busch, Andrew W.
    Year published
    2021
    Metadata
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    Abstract
    Protein secondary structure prediction has been a long-standing problem in computational biology. Recent advances in deep contextual learning have enabled its performance in three-state prediction closer to the theoretical limit at 88–90%. Here, we showed that a large training set with 95% sequence identity cutoff can improve prediction of secondary structures even for those unrelated test sequences (<25% sequence identity cutoff) compared to the use of a non-redundant training dataset with 25% sequence identity cutoff. The three-state prediction edges closer to an accuracy of 87% and eight-state at 76%.The resulting model ...
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    Protein secondary structure prediction has been a long-standing problem in computational biology. Recent advances in deep contextual learning have enabled its performance in three-state prediction closer to the theoretical limit at 88–90%. Here, we showed that a large training set with 95% sequence identity cutoff can improve prediction of secondary structures even for those unrelated test sequences (<25% sequence identity cutoff) compared to the use of a non-redundant training dataset with 25% sequence identity cutoff. The three-state prediction edges closer to an accuracy of 87% and eight-state at 76%.The resulting model called SPOT-1D2 is freely available to academic users at https://github.com/jas-preet/SPOT-1D2.
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    Conference Title
    2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
    DOI
    https://doi.org/10.1109/cibcb49929.2021.9562849
    Funder(s)
    ARC
    Grant identifier(s)
    DP210101875
    Subject
    Bioinformatics and computational biology
    Publication URI
    http://hdl.handle.net/10072/411118
    Collection
    • Conference outputs

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