Skip to content
Fowler Lab
Fowler Lab

Predicting antimicrobial resistance

  • News
  • Publications
  • Members
  • Research
    • Overview
    • Manifesto
    • Software
    • Reproducibility
  • Teaching
  • Contact
    • PhDs
  • Wiki
Fowler Lab
Fowler Lab

Predicting antimicrobial resistance

AMyGDA now available from GitHub

Philip Fowler, 27th January 202027th January 2020

AMyGDA is a python module that analyses photographs of 96-well plates and, by examining each well for bacterial growth, is able to read a series of minimum inhibitory concentrations for the antibiotics present on a plate.

Previously it was only available to download from this website (due to licensing) if you gave your email address which was inconvenient and also meant the public version often lagged the current stable release.

Now it is available directly from GitHub!

Share this:

  • Share on X (Opens in new window) X
  • Share on Bluesky (Opens in new window) Bluesky
  • Email a link to a friend (Opens in new window) Email
  • Share on LinkedIn (Opens in new window) LinkedIn
  • Share on Mastodon (Opens in new window) Mastodon

Related

antimicrobial resistance clinical microbiology computing tuberculosis

Post navigation

Previous post
Next post

Related Posts

computing

GROMACS 4.6

18th October 201323rd September 2018

GROMACS is a scientific code designed to simulate the dynamics of small boxes of stuff, that…

Share this:

  • Share on X (Opens in new window) X
  • Share on Bluesky (Opens in new window) Bluesky
  • Email a link to a friend (Opens in new window) Email
  • Share on LinkedIn (Opens in new window) LinkedIn
  • Share on Mastodon (Opens in new window) Mastodon
Read More
computing

Software Carpentry Feedback

1st November 2012

As well as asking the attendees how they thought the workshop had gone, I sent them…

Share this:

  • Share on X (Opens in new window) X
  • Share on Bluesky (Opens in new window) Bluesky
  • Email a link to a friend (Opens in new window) Email
  • Share on LinkedIn (Opens in new window) LinkedIn
  • Share on Mastodon (Opens in new window) Mastodon
Read More
antimicrobial resistance

New preprint: Predicting pyrazinamide resistance in M. tuberculosis using a graph convolutional network

29th October 202530th October 2025

In previous work we’ve used “traditional” machine-learning approaches, like XGBoost, to learn and therefore predict…

Share this:

  • Share on X (Opens in new window) X
  • Share on Bluesky (Opens in new window) Bluesky
  • Email a link to a friend (Opens in new window) Email
  • Share on LinkedIn (Opens in new window) LinkedIn
  • Share on Mastodon (Opens in new window) Mastodon
Read More

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Privacy & Cookies: This site uses cookies. By continuing to use this website, you agree to their use.

To find out more, including how to control cookies, see here: Cookie Policy
    ©2026 Fowler Lab | WordPress Theme by SuperbThemes

    Loading Comments...