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.
Guardado en:
Autores principales: | Kengo Sato, Manato Akiyama, Yasubumi Sakakibara |
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Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
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Materias: | |
Acceso en línea: | https://doaj.org/article/862e961ec971484fbb3f7229d26241f6 |
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