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
antimicrobial resistance New preprint: compensatory mutations are associated with increased growth in resistant samples of M. tuberculosis. 22nd June 20238th December 2023 In this preprint, Viki Brunner shows how, using the large CRyPTIC dataset, she can recapitulate… 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: Assessing Drug Susceptibility in Tuberculosis 28th September 201829th September 2018 A paper was published in the New England Journal of Medicine earlier this week by… 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 publication: CRyPTIC Data Compendium 16th August 202216th August 2022 The large and comprehensive dataset of clinical tuberculosis isolates collected by the CRyPTIC project is… 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