Deep learning models for RNA secondary structure prediction (probably) do not generalize across families | Zendy

Marcell Szikszai | Zendy; Michael J. Wise | Zendy; Amitava Datta | Zendy; Max Ward | Zendy; David H. Mathews | Zendy

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

Author(s) -

Marcell Szikszai,

Michael J. Wise,

Amitava Datta,

Max Ward,

David H. Mathews

Publication year - 2022

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btac415

Subject(s) - generalization , computer science , source code , machine learning , artificial intelligence , code (set theory) , nucleic acid secondary structure , function (biology) , deep learning , convolutional neural network , rna , theoretical computer science , data mining , mathematics , programming language , biology , mathematical analysis , biochemistry , set (abstract data type) , evolutionary biology , gene

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.

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