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Reply to: LncADeep performance on full-length transcripts

Nature Machine Intelligence volume 3, page 196 (2021)Cite this article

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The Original Article was published on 13 January 2020

replying to C. Yang et al. Nature Machine Intelligence https://doi.org/10.1038/s42256-019-0108-2 (2019)

Long non-coding RNA (lncRNA) is involved in crucial biological processes and contributes to many diseases such as cancer. Recently, deep learning has been applied to the identification of lncRNAs to alleviate their costly and time-consuming experimental verification. In our paper1, we review and evaluate the following deep learning lncRNA tools: lncRNAnet2, LncADeep3 and lncFinder4. Zhu and co-workers5 have highlighted that LncADeep3 provides two different models for full- and partial-length transcripts and that in our paper1 we have used the full-length model of LncADeep instead of the default partial-length model.

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Data availability

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

References

  1. Amin, N., McGrath, A. & Chen, Y.-P. P. Evaluation of deep learning in non-coding RNA classification. Nat. Mach. Intell. 1, 246–256 (2019).

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  2. Baek, J., Lee, B., Kwon, S. & Yoon, S. LncRNAnet: long non-coding RNA identification using deep learning. Bioinformatics 34, 3889–3897 (2018).

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  3. Yang, C. et al. LncADeep: an ab initio lncRNA identification and functional annotation tool based on deep learning. Bioinformatics 34, 3825–3834 (2018).

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  4. Han, S. et al. LncFinder: an integrated platform for long non-coding RNA identification utilizing sequence intrinsic composition, structural information and physicochemical property. Brief Bioinform. https://doi.org/10.1093/bib/bby065 (2018).

  5. Yang, C., Zhou, M., Xie, H. & Zhu, H. LncADeep performance on full-length transcripts. Nat. Mach. Intell. https://doi.org/10.1038/s42256-019-0108-2, (2019).

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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

Authors
  1. Noorul Amin
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  2. Annette McGrath
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  3. Yi-Ping Phoebe Chen
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Correspondence to Yi-Ping Phoebe Chen.

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Amin, N., McGrath, A. & Chen, YP.P. Reply to: LncADeep performance on full-length transcripts. Nat Mach Intell 3, 196 (2021). https://doi.org/10.1038/s42256-019-0107-3

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