An accessible infrastructure for artificial intelligence using a Docker-based JupyterLab in Galaxy.
Anup KumarGianmauro CuccuruBjoern Andreas GrueningRolf BackofenPublished in: GigaScience (2023)
These features make JupyterLab in Galaxy Europe highly suitable for creating and managing AI projects. A recent scientific publication that predicts infected regions in COVID-19 computed tomography scan images is reproduced using various features of JupyterLab on Galaxy Europe. In addition, ColabFold, a faster implementation of AlphaFold2, is accessed in JupyterLab to predict the 3-dimensional structure of protein sequences. JupyterLab is accessible in 2 ways-one as an interactive Galaxy tool and the other by running the underlying Docker container. In both ways, long-running training can be executed on Galaxy's compute infrastructure. Scripts to create the Docker container are available under MIT license at https://github.com/usegalaxy-eu/gpu-jupyterlab-docker.
Keyphrases
- artificial intelligence
- computed tomography
- deep learning
- machine learning
- big data
- coronavirus disease
- high intensity
- sars cov
- quality improvement
- primary care
- positron emission tomography
- magnetic resonance imaging
- convolutional neural network
- healthcare
- amino acid
- optical coherence tomography
- dual energy
- small molecule