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Preprints, Working Papers, ... Year : 2023

Comparison and benchmark of deep learning methods for non-coding RNA classification

Abstract

The grouping of non-coding RNAs into functional classes started in the 1950s with housekeeping RNAs. Since, multiple additional classes were described. The involvement of non-coding RNAs in biological processes and diseases has made their characterization crucial, creating a need for computational methods that can classify large sets of non-coding RNAs. In recent years, the success of deep learning in various domains led to its application to non-coding RNA classification. Multiple novel architectures have been developed, but these advancements are not covered by current literature reviews. We propose a comparison of the different approaches and of non-coding RNA datasets proposed in the state-of-the-art. Then, we perform experiments to fairly evaluate the performance of various tools for non-coding RNA classification on two popular datasets. With regard to these results, we assess the relevance of the different architectural choices and provide recommendations to consider in future methods.
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hal-04437995 , version 1 (05-02-2024)

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Constance Creux, Farida Zehraoui, François Radvanyi, Fariza Tahi. Comparison and benchmark of deep learning methods for non-coding RNA classification. 2024. ⟨hal-04437995⟩
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