<HashMap><database>biostudies-other</database><scores/><additional><omics_type>Unknown</omics_type><volume>11</volume><submitter>Sucheta Ghosh</submitter><journal>GigaScience</journal><pagination>giac077</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/MODEL2304210001</full_dataset_link><repository>biostudies-other</repository><additional_accession>10.1093/gigascience/giac077</additional_accession><pubmed_authors>Sucheta Ghosh</pubmed_authors></additional><is_claimable>false</is_claimable><name>Nassar2022 - Metagenomics Classification Task for Scientific Literature Text</name><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&amp;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</description><dates><release>2023-05-11T00:00:00Z</release><modification>2025-07-14T17:06:15.903Z</modification><creation>2025-03-31T13:21:44.302Z</creation></dates><accession>MODEL2304210001</accession><cross_references><doi>10.1093/gigascience/giac077</doi></cross_references></HashMap>