Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • News & Views
  • Published:

Machine learning

Predicting pathogenicity from non-coding mutations

Nature Biomedical Engineering volume 7, pages 709–710 (2023)Cite this article

Subjects

A machine-learning model identifies cell-type-specific predictors of the pathogenic effects of promoter and enhancer mutations in the human genome.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Non-coding regulatory regions that mediate transcription.

References

  1. Rentzsch, P. et al. Nucleic Acids Res. 47, D886–D894 (2019).

    Article  CAS  PubMed  Google Scholar 

  2. Shihab, H. et al. Bioinformatics. 31, 1536–1543 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Quang, D., Chen, Y. & Xiaohui, X. Bioinformatics 31, 761–763 (2015).

    Article  CAS  PubMed  Google Scholar 

  4. Frazer, J. et al. Nature 599, 91–95 (2021).

    Article  CAS  PubMed  Google Scholar 

  5. Ioannidis, N. et al. Am. J. Hum. Genet. 99, 877–885 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Jagadeesh, K. et al. Nat. Genet. 48, 1581–1586 (2016).

    Article  CAS  PubMed  Google Scholar 

  7. Ferlaino, M. et al. BMC Bioinformatics 18, 442 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Koido, M. et al. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-022-00961-8 (2022).

    Article  PubMed  Google Scholar 

  9. Finucane, H. et al. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Grimm, D. et al. Hum. Mutat. 36, 513–523 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ritchie, G. et al. Nat. Methods 11, 294–296 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Intelligent Systems Laboratory, University of Bristol, Bristol, UK

    Colin Campbell

  2. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK

    Amy Francis & Tom R. Gaunt

Authors
  1. Colin Campbell
    View author publications

    Search author on:PubMed Google Scholar

  2. Amy Francis
    View author publications

    Search author on:PubMed Google Scholar

  3. Tom R. Gaunt
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Colin Campbell.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Campbell, C., Francis, A. & Gaunt, T.R. Predicting pathogenicity from non-coding mutations. Nat. Biomed. Eng 7, 709–710 (2023). https://doi.org/10.1038/s41551-022-00996-x

Download citation

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41551-022-00996-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing