Abstract
AbstractThe 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.
Publisher
Cold Spring Harbor Laboratory