RNA secondary structure prediction using deep learning with thermodynamic integration

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.

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Auteurs principaux: Kengo Sato, Manato Akiyama, Yasubumi Sakakibara
Format: article
Langue:EN
Publié: Nature Portfolio 2021
Sujets:
Q
Accès en ligne:https://doaj.org/article/862e961ec971484fbb3f7229d26241f6
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