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

doi:10.1101/2022.03.21.485135

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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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ORCID record for David H. Mathews

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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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