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Deep learning models for RNA secondary structure prediction (probably) do not generalise across families

View ORCID ProfileMarcell Szikszai, View ORCID ProfileMichael Wise, View ORCID ProfileAmitava Datta, View ORCID ProfileMax Ward, View ORCID ProfileDavid H. Mathews
doi: https://doi.org/10.1101/2022.03.21.485135
Marcell Szikszai
1Department of Computer Science & Software Engineering, The University of Western Australia, Perth, WA, Australia
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  • For correspondence: marcell.szikszai@research.uwa.edu.au
Michael Wise
1Department of Computer Science & Software Engineering, The University of Western Australia, Perth, WA, Australia
2The Marshall Centre for Infectious Diseases Research and Training, The University of Western Australia, Perth, WA, Australia
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Amitava Datta
1Department of Computer Science & Software Engineering, The University of Western Australia, Perth, WA, Australia
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Max Ward
1Department of Computer Science & Software Engineering, The University of Western Australia, Perth, WA, Australia
3Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
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David H. Mathews
4Department of Biochemistry & Biophysics, Center for RNA Biology, and Department of Biostatistics & Computational Biology, University of Rochester, Rochester, NY, USA
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Abstract

Motivation The secondary structure of RNA is of importance to its function. Over the last few years, several papers attempted to use machine learning to improve de novo RNA secondary structure prediction. Many of these papers report impressive results for intra-family predictions, but seldom address the much more difficult (and practical) inter-family problem.

Results We demonstrate it is nearly trivial with convolutional neural networks to generate pseudo-free energy changes, modeled after structure mapping data, that improve the accuracy of structure prediction for intra-family cases. We propose a more rigorous method for inter-family cross-validation that can be used to assess the performance of learning-based models. Using this method, we further demonstrate that intra-family performance is insufficient proof of generalisation despite the widespread assumption in the literature, and provide strong evidence that many existing learning-based models have not generalised inter-family.

Availability Source code and data is available at https://github.com/marcellszi/dl-rna.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/marcellszi/dl-rna

  • 1 16S rRNA and 23S rRNA are split into independent folding domains. [40]

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 21, 2022.
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Deep learning models for RNA secondary structure prediction (probably) do not generalise across families
Marcell Szikszai, Michael Wise, Amitava Datta, Max Ward, David H. Mathews
bioRxiv 2022.03.21.485135; doi: https://doi.org/10.1101/2022.03.21.485135
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Deep learning models for RNA secondary structure prediction (probably) do not generalise across families
Marcell Szikszai, Michael Wise, Amitava Datta, Max Ward, David H. Mathews
bioRxiv 2022.03.21.485135; doi: https://doi.org/10.1101/2022.03.21.485135

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