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.

Deep learning for biology

A popular artificial-intelligence method provides a powerful tool for surveying and classifying biological data. But for the uninitiated, the technology poses significant difficulties.
  1. Sarah Webb

    1. Sarah Webb is a freelance writer in Chattanooga, Tennessee.

    View author publications

    You can also search for this author in PubMed  Google Scholar

Four years ago, scientists from Google showed up on neuroscientist Steve Finkbeiner’s doorstep. The researchers were based at Google Accelerated Science, a research division in Mountain View, California, that aims to use Google technologies to speed scientific discovery. They were interested in applying ‘deep-learning’ approaches to the mountains of imaging data generated by Finkbeiner’s team at the Gladstone Institute of Neurological Disease in San Francisco, also in California.

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

Nature 554, 555-557 (2018)

doi: https://doi.org/10.1038/d41586-018-02174-z

Updates & Corrections

  • Correction 07 March 2018: An earlier version of this story affiliated Mark DePristo with Verily Life Sciences. It also omitted to mention that the DeepVariant tool was developed jointly by Verily and Google.

Subjects

Latest on:

Nature Careers

Jobs

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

Search

Advanced search

Quick links