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  • Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks

    Author(s)
    Hanson, Jack
    Yang, Yuedong
    Paliwal, Kuldip
    Zhou, Yaoqi
    Griffith University Author(s)
    Paliwal, Kuldip K.
    Hanson, Jack S.
    Zhou, Yaoqi
    Yang, Yuedong
    Year published
    2017
    Metadata
    Show full item record
    Abstract
    Motivation: Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accuracy of speech and image classification problems by remembering useful past information in long sequential events. Here, we have implemented deep bidirectional LSTM recurrent neural networks in the problem of protein intrinsic disorder prediction. Results: The new method, named SPOT-Disorder, has steadily improved over a similar method using a traditional, window-based neural network ...
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    Motivation: Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accuracy of speech and image classification problems by remembering useful past information in long sequential events. Here, we have implemented deep bidirectional LSTM recurrent neural networks in the problem of protein intrinsic disorder prediction. Results: The new method, named SPOT-Disorder, has steadily improved over a similar method using a traditional, window-based neural network (SPINE-D) in all datasets tested without separate training on short and long disordered regions. Independent tests on four other datasets including the datasets from critical assessment of structure prediction (CASP) techniques and >10 000 annotated proteins from MobiDB, confirmed SPOT-Disorder as one of the best methods in disorder prediction. Moreover, initial studies indicate that the method is more accurate in predicting functional sites in disordered regions. These results highlight the usefulness combining LSTM with deep bidirectional recurrent neural networks in capturing non-local, long-range interactions for bioinformatics applications.
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    Journal Title
    Bioinformatics
    Volume
    33
    Issue
    5
    DOI
    https://doi.org/10.1093/bioinformatics/btw678
    Subject
    Information and Computing Sciences not elsewhere classified
    Mathematical Sciences
    Biological Sciences
    Information and Computing Sciences
    Publication URI
    http://hdl.handle.net/10072/346675
    Collection
    • Journal articles

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