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Open Access

Peer-reviewed

Research Article

A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification

Loïc Omnes,

Roles Conceptualization, Data curation, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

Affiliations Université Paris-Saclay, Univ Evry, IBISC, 91020 Evry-Courcouronnes, France, ADLIN Science, 91037 Evry-Courcouronnes, France

https://orcid.org/0000-0002-7427-582X

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Eric Angel,

Roles Supervision, Writing – review & editing

Affiliation Université Paris-Saclay, Univ Evry, IBISC, 91020 Evry-Courcouronnes, France
⨯
Pierre Bartet,

Roles Funding acquisition, Methodology, Supervision, Writing – review & editing

Affiliation ADLIN Science, 91037 Evry-Courcouronnes, France
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François Radvanyi,

Roles Supervision, Writing – review & editing

Affiliation Molecular Oncology UMR144, CNRS - Institut Curie, 75005 Paris, France
⨯
Fariza Tahi

Roles Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – review & editing

* E-mail: fariza.tahi@univ-evry.fr

Affiliation Université Paris-Saclay, Univ Evry, IBISC, 91020 Evry-Courcouronnes, France

https://orcid.org/0000-0002-3975-5668

⨯

A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification

Loïc Omnes,
Eric Angel,
Pierre Bartet,
François Radvanyi,
Fariza Tahi

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Published: April 25, 2025
https://doi.org/10.1371/journal.pone.0314837

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Pseudoknots
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RNA structure
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RNA structure prediction
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Forecasting
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RNA sequences
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Sequence motif analysis
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Deep learning
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Ribosomal RNA
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