Improving RNA secondary structure prediction via state inference with deep recurrent neural networks

DOI10.1515/CMB-2020-0002zbMath1439.92147arXiv1906.10819OpenAlexW3014154699MaRDI QIDQ2183366

Devin T. Willmott, David Murrugarra, Qiang Ye

Publication date: 27 May 2020

Published in: Computational and Mathematical Biophysics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1906.10819

zbMATH Keywords

recurrent neural networksdeep learningNNTMRNA secondary structure inferenceRNA state inferenceSHAPE-directed NNTM

Mathematics Subject Classification ID

Protein sequences, DNA sequences (92D20)

Related Items (1)

rna-state-inf

Uses Software

• LSTM
• Theano
• ViennaRNA
• UNAFold
• Keras
• RNAstructure
• RMSprop
• GitHub
• NNDB
• reactIDR
• patteRNA

Cites Work

• The rainbow spectrum of RNA secondary structures
• Biological Sequence Analysis
• Learning representations by back-propagating errors
• Unnamed Item

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