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Improving RNA secondary structure prediction via state inference with deep recurrent neural networks by Willmott, Devin ;Murrugarra, David ;Ye, Qiang is a Medicine article available to read on EtoBox.

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Abstract The problem of determining which nucleotides of an RNA sequence are paired or unpaired in the secondary structure of an RNA, which we call RNA state inference, can be studied by different machine learning techniques. Successful state inference of RNA sequences can be used to generate auxiliary information for data-directed RNA secondary structure prediction. Typical tools for state inference, such as hidden Markov models, exhibit poor performance in RNA state inference, owing in part to their inability to recognize nonlocal dependencies. Bidirectional long short-term memory (LSTM) neural networks have emerged as a powerful tool that can model global nonlinear sequence dependencies and have achieved state-of-the-art performances on many different classification problems. This paper presents a practical approach to RNA secondary structure inference centered around a deep learning method for state inference. State predictions from a deep bidirectional LSTM are used to generate synthetic SHAPE data that can be incorporated into RNA secondary structure prediction via the Nearest Neighbor Thermodynamic Model (NNTM). This method produces predicted secondary structures for a diver

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Author

Willmott, Devin ;Murrugarra, David ;Ye, Qiang

Publisher

Walter de Gruyter GmbH

Published

2020

Language

EN

Field

Medicine (Physical Sciences)