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Evaluation of deep learning in non-coding RNA classification

Nature Machine Intelligence volume 1, pages 246–256 (2019)Cite this article

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An Author Correction to this article was published on 13 January 2020

Matters Arising to this article was published on 13 January 2020

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Abstract

Non-coding (nc) RNA plays a vital role in biological processes and has been associated with diseases such as cancer. Classification of ncRNAs is necessary for understanding the underlying mechanisms of the diseases and to design effective treatments. Recently, deep learning has been employed for ncRNA identification and classification and has shown promising results. In this study, we review the progress of ncRNA type classification, specifically lncRNA, lincRNA, circular RNA and small ncRNA, and present a comprehensive comparison of six deep learning based classification methods published in the past two years. We identify research gaps and challenges of ncRNA types, such as the classification of subclasses of lncRNA, transcript length and compositional variation, dependency on database searches and the high false positive rate of existing approaches. We suggest future directions for cross-species performance deviation, deep learning model selection and sequence intrinsic features.

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Fig. 1: Overall taxonomy of ncRNA.
Fig. 2: Architectures of deep learning models.
Fig. 3: Length distribution of the long non-coding and protein-coding transcripts in human and mouse datasets.
Fig. 4: ROC curves for the lncRNA classification algorithms.
Fig. 5: Precision recall curves for the lncRNA classification algorithms.
Fig. 6

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Data availability

The dataset, source code and usage instructions are available at http://homepage.cs.latrobe.edu.au/ypchen/ncRNAanalysis/.

Change history

  • 13 January 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Authors and Affiliations

  1. Department of Computer Science and Information Technology, La Trobe University, Melbourne, Victoria, Australia

    Noorul Amin & Yi-Ping Phoebe Chen

  2. Data61, CSIRO, Brisbane, Queensland, Australia

    Noorul Amin & Annette McGrath

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  1. Noorul Amin
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Contributions

All authors contributed to the manuscript. N.A., Y.P.C. and A.M. conceived the idea. N.A. implemented the code, performed experiments and wrote the paper. A.M. and Y.P.C. contributed to the write up and with experiment analysis. A.M. and Y.P.C. reviewed the article.

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Correspondence to Yi-Ping Phoebe Chen.

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Amin, N., McGrath, A. & Chen, YP.P. Evaluation of deep learning in non-coding RNA classification. Nat Mach Intell 1, 246–256 (2019). https://doi.org/10.1038/s42256-019-0051-2

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