In the rapidly evolving field of bioimaging, the integration and orchestration of findable, accessible, interoperable, and reusable (FAIR) image analysis workflows remains a challenge. We introduce BIOMERO (bioimage analysis in OMERO), a bridge connecting OMERO, a renowned bioimaging data management platform; FAIR workflows; and high-performance computing (HPC) environments. BIOMERO facilitates seamless execution of FAIR workflows, particularly for large datasets from high-content or high-throughput screening. BIOMERO empowers researchers by eliminating the need for specialized knowledge, enabling scalable image processing directly from OMERO. BIOMERO notably supports the sharing and utilization of FAIR workflows between OMERO, Cytomine/BIAFLOWS, and other bioimaging communities. BIOMERO will promote the widespread adoption of FAIR workflows, emphasizing reusability, across the realm of bioimaging research. Its user-friendly interface will empower users, including those without technical expertise, to seamlessly apply these workflows to their datasets, democratizing the utilization of AI by the broader research community.
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http://dx.doi.org/10.1016/j.patter.2024.101024 | DOI Listing |
Phys Eng Sci Med
January 2025
School of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia.
Artificial Intelligence (AI) based auto-segmentation has demonstrated numerous benefits to clinical radiotherapy workflows. However, the rapidly changing regulatory, research, and market environment presents challenges around selecting and evaluating the most suitable solution. To support the clinical adoption of AI auto-segmentation systems, Selection Criteria recommendations were developed to enable a holistic evaluation of vendors, considering not only raw performance but associated risks uniquely related to the clinical deployment of AI.
View Article and Find Full Text PDFElife
January 2025
Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom.
Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites, and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these biological processes makes the construction and reuse of biologically detailed models challenging. A wide range of tools have been developed to aid their construction and simulation, but differences in design and internal representation act as technical barriers to those who wish to use data-driven models in their research workflows.
View Article and Find Full Text PDFbioRxiv
December 2024
IFB-core, Institut Français de Bioinformatique, CNRS, INSERM, INRAE, CEA, Evry, France.
Microbial research generates vast and complex data from diverse omics technologies, necessitating innovative analytical solutions. microGalaxy (Galaxy for Microbiology) addresses these needs with a user-friendly platform that integrates >220 tool suites and >65 curated workflows for microbial analyses, including taxonomic profiling, assembly, annotation, and functional analysis. Hosted on the main EU Galaxy server (microgalaxy.
View Article and Find Full Text PDFJ Biomed Semantics
December 2024
Medical BioSciences Department, Radboud University Medical Center, Nijmegen, The Netherlands.
Motivation: We are witnessing an enormous growth in the amount of molecular profiling (-omics) data. The integration of multi-omics data is challenging. Moreover, human multi-omics data may be privacy-sensitive and can be misused to de-anonymize and (re-)identify individuals.
View Article and Find Full Text PDFClin Genet
December 2024
Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
Broad-spectrum genetic tests often lead to the identification of variants of uncertain significance (VUS), a major issue in modern clinical genetics. A fair proportion of VUS may alter the splicing processes, but their interpretation is challenging. This study aimed at providing a classification approach for VUS potentially-affecting splicing by integrating transcript analysis from peripheral blood mRNA into routine diagnostics.
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