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

ABSTRACT: The involvement of non-coding RNAs in biological processes and diseases has made the exploration of their functions crucial. Most non-coding RNAs have yet to be studied, creating the need for methods that can rapidly classify large sets of non-coding RNAs into functional groups, or classes. 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 present an exhaustive comparison of the different methods proposed in the state-of-the-art and describe their associated datasets. Moreover, the literature lacks objective benchmarks. We perform experiments to fairly evaluate the performance of various tools for

SUBMITTER: Creux C 

PROVIDER: S-EPMC11421803 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature