Skip to main content

Thank you for visiting 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.

Setting the standards for machine learning in biology

Machine learning is a branch of artificial intelligence (AI) involving computer programs that are able to improve their own performance through experience (training). The diverse applications of new ‘deep learning’ approaches with neural networks are now expanding into the field of biology. But these applications to biological data require more scrutiny and caution to increase the standards of publishing and allow the AI revolution in biology to take off.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    Min, S., Lee, B. & Yoon, S. Deep learning in bioinformatics. Brief. Bioinf. 18, 851–869 (2017).

    Google Scholar 

  2. 2.

    Stormo, G. D., Schneider, T. D., Gold, L. & Ehrenfeuch, A. Use of the perceptron algorithm to distinguish translation initiation sites in E. coli. Nucleic Acids Res. 10, 2997–3011 (1982).

    CAS  Article  Google Scholar 

  3. 3.

    Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K. & Greene, C. S. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15, 20170387 (2017).

    Article  Google Scholar 

  4. 4.

    AlQuraishi, M. AlphaFold at CASP13. Bioinformatics, btz422 (2019).

  5. 5.

    Walsh, I., Pollastri, G. & Tosatto, S. C. E. Correct machine learning on protein sequences: a peer-reviewing perspective. Brief. Bioinf. 17, 831–840 (2016).

    CAS  Article  Google Scholar 

  6. 6.

    Chicco, D. Ten quick tips for machine learning in computational biology. BioData Min. 10, 35 (2017).

    Article  Google Scholar 

  7. 7.

    Tabe-Bordbar, S., Emad, A., Zhao, S. D. & Sinha, S. A closer look at cross-validation for assessing the accuracy of gene regulatory networks and models. Sci. Rep. 8, 6620 (2018).

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to David T. Jones.

Ethics declarations

Competing interests

The author declares no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jones, D.T. Setting the standards for machine learning in biology. Nat Rev Mol Cell Biol 20, 659–660 (2019).

Download citation

Further reading


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