Therefore, running software in a container is more secure. A container runs in an isolated environment having minimal interactions with the host OS. Software executing inside a Docker container is abstracted from the host operating system (OS) as most of the requirements necessary for them to run successfully are already configured inside its container. ĭocker containers are popular for shipping packaged software as complete ecosystems, enabling them to be reproducible in a platform-independent manner. The infrastructure consists of 3 major components: first, a Docker container that encapsulates JupyterLab together with multiple packages and plugins used for developing AI programs, data manipulation, and visualization (section S2 in the supplementary file lists all such packages and plugins with their respective versions) second, a Galaxy interactive tool that downloads this Docker container to serve JupyterLab on Galaxy Europe and third, the compute infrastructure of Galaxy Europe and the de.NBI cloud. To facilitate such tasks, a complete infrastructure is developed that combines JupyterLab, augmented with many useful features, running on the public compute infrastructure of Galaxy Europe to perform end-to-end AI analyses on scientific datasets. They include preprocessing raw datasets to transform them into suitable formats that are compatible with ML algorithms, creating and executing their complex architectures on preprocessed datasets, and making trained models and predicted datasets readily available for further analyses. To be able to use ML algorithms on such datasets, a robust and efficient compute infrastructure is needed that can serve multiple purposes. Machine learning (ML) approaches are being increasingly used with these datasets for predictive tasks such as medical diagnosis, imputing missing features, augmenting datasets with artificially generated ones, estimating gene expression patterns, and many more. Next-generation sequencing generates DNA sequences that are stored as FASTA and FASTQ files. The medical imaging field generates images of cells and tissues, radiography images such as chest x-rays, and computed tomography (CT) scans. For example, the single-cell field creates gene expression patterns for each cell that are represented as matrices of real numbers. Bioinformatics comprises many subfields, such as single cell, medical imaging, sequencing, proteomics, and many more, that produce a huge amount of biological data in myriad formats.
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