{"database":"biostudies-other","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["11"],"submitter":["Sucheta Ghosh"],"journal":["GigaScience"],"pagination":["giac077"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/MODEL2304210001"],"repository":["biostudies-other"],"additional_accession":["10.1093/gigascience/giac077"],"pubmed_authors":["Sucheta Ghosh"]},"is_claimable":false,"name":"Nassar2022 - Metagenomics Classification Task for Scientific Literature Text","description":"This is a use case to show that, given any automatic metagenomic classification model for the documents, we can convert those to ONNX (Open Neural Network Exchange) format; it also consists of the Dockerfile that can be used to prepare a docker image.  This conversion ensures interoperability and open access. The ONNX format utility can perform the following essential tasks: model conversion, inference, inspection, and optimization. Reference: 1) https://github.com/elixir-europe/biohackathon-projects-2022/tree/main/9 2) https://www.ebi.ac.uk/biomodels/search?query=Maaly+Nassar&domain=biomodels 3) https://gitlab.com/maaly7/emerald_metagenomics_annotations 4) This model is built upon the model of the following publication: Maaly Nassar, Alexander B Rogers, Francesco Talo', Santiago Sanchez, Zunaira Shafique, Robert D Finn, Johanna McEntyre, A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications, GigaScience, Volume 11, 2022, giac077, https://doi.org/10.1093/gigascience/giac077","dates":{"release":"2023-05-11T00:00:00Z","modification":"2025-07-14T17:06:15.903Z","creation":"2025-03-31T13:21:44.302Z"},"accession":"MODEL2304210001","cross_references":{"doi":["10.1093/gigascience/giac077"]}}