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...

Full description

Bibliographic Details

Published in:	Proceedings / Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE) pp. 14 - 21
Main Authors:	Omnes, Loic, Angel, Eric, Bartet, Pierre, Radvanyi, Francois, Tahi, Fariza
Format:	Conference Proceeding
Language:	English
Published:	IEEE 04.12.2023
Subjects:	Bioinformatics Complexity theory Computational modeling deep learning long non-coding RNA Medical services Predictive models RNA secondary structure prediction Training
ISSN:	2471-7819
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!