Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction

Xiangyun Qiu

doi:10.1371/journal.pcbi.1011047

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Open Access

Peer-reviewed

Research Article

Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction

Xiangyun Qiu

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: xqiu@gwu.edu

Affiliation Department of Physics, George Washington University, Washington DC, United States of America
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Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction

Xiangyun Qiu

Published: April 17, 2023
https://doi.org/10.1371/journal.pcbi.1011047

About the Authors

Xiangyun Qiu

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: xqiu@gwu.edu

Affiliation Department of Physics, George Washington University, Washington DC, United States of America

Competing Interests

The author declares no conflict of interest.

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RNA structure
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RNA sequences
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Machine learning
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Sequence alignment
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Learning
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RNA folding
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RNA alignment
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RNA structure prediction
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