Nature Communications (Feb 2021)

RNA secondary structure prediction using deep learning with thermodynamic integration

  • Kengo Sato,
  • Manato Akiyama,
  • Yasubumi Sakakibara

DOI
https://doi.org/10.1038/s41467-021-21194-4
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 9

Abstract

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Accurately predicting the secondary structure of non-coding RNAs can help unravel their function. Here the authors propose a method integrating thermodynamic information and deep learning to improve the robustness of RNA secondary structure prediction compared to several existing algorithms.