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

Jack Hanson | Zendy; Yuedong Yang | Zendy; Kuldip K. Paliwal | Zendy; Yaoqi Zhou | Zendy

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

Author(s) -

Jack Hanson,

Yuedong Yang,

Kuldip K. Paliwal,

Yaoqi Zhou

Publication year - 2016

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btw678

Subject(s) - computer science , artificial neural network , artificial intelligence , web server , deep learning , casp , machine learning , recurrent neural network , long short term memory , range (aeronautics) , data mining , pattern recognition (psychology) , protein structure prediction , the internet , protein structure , biology , biochemistry , world wide web , materials science , composite material

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.

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