Prediction of Secondary Structure for Long Non-Coding RNAs using a Recursive Cutting Method based on Deep Learning

Accurately predicting the secondary structure of RNA, particularly for long non-coding RNA, has direct implications in healthcare, where it can be used for diagnostic, therapeutic, and drug discovery purposes. However, the majority of previous approaches are too costly in terms of computation budget...

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Veröffentlicht in:	Proceedings / Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE) S. 14 - 21
Hauptverfasser:	Omnes, Loic, Angel, Eric, Bartet, Pierre, Radvanyi, Francois, Tahi, Fariza
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 04.12.2023
Schlagworte:	Bioinformatics Complexity theory Computational modeling deep learning long non-coding RNA Medical services Predictive models RNA secondary structure prediction Training
ISSN:	2471-7819
Online-Zugang:	Volltext
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