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 publication: Automated detection of bacterial growth on 96-well plates for high-throughput drug susceptibility testing of M. tuberculosis 26th October 2018 In this Microbiology paper we show how a Python package, called the Automated Mycobacterial Detection Growth… 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 GROMACS on AWS: Performance and Cost 17th January 20163rd March 2019 So we have created an Amazon Machine Image (AMI) with GROMACS installed. In this post… 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 Postdoctoral position advertised 17th May 202117th May 2021 Through the CompBioMed2 EU Centre of Excellence project I have funding to appoint a postdoctoral… 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