New paper: a deep learning model that reads MICs from images of 96 well plates Philip Fowler, 26th May 20251st July 2025 Our paper describing how a convolutional neural network model can determine the minimum inhibitory concentrations (MICs) from a photograph of the 96-well plate after two weeks incubation has been published in the Computational and Structural Biology Journal. You can get the model, which is called TMAS, on GitHub here and there is a longer description here. 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 computing tuberculosis
New preprint: Including minor alleles improves fluoroquinolone resistance prediction 10th November 202217th November 2022 Fluoroquinolones are used to treat both normal and drug resistant tuberculosis and therefore being able… 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
New grant: Ox4TB 17th March 202517th March 2025 Very pleased to announce that I am a co-investigator on the recently announced Oxford4TB project… 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 Publication: Predicting whether mutations confer resistance to an antibiotic 5th January 201829th September 2018 Due to the rise of antibiotic resistance, it is increasingly important that your clinician knows… 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