Dataset Information

Nassar2022 - Metagenomics Classification Task for Scientific Literature Text

ABSTRACT: 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

SUBMITTER: Sucheta Ghosh

PROVIDER: BIOMD0000001068 | BioModels | 2023-05-25

REPOSITORIES: BioModels

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			Action	DRS
	BIOMD0000001068?filename=doc2vec-model.onnx	Other
	BIOMD0000001068?filename=onnx-metagene-text-classifier.zip	Other

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Project description:The Cancer Genome Atlas (TCGA) is a comprehensive and coordinated effort to accelerate our understanding of the molecular basis of cancer through the application of genome analysis technologies, including large-scale genome sequencing. TCGA is a joint effort of the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI), which are both part of the National Institutes of Health, U.S. Department of Health and Human Services. TCGA projects are organized by cancer type or subtype. Click <a href="http://cancergenome.nih.gov/cancersselected" target="_blank">here</a> for a current list of cancer types selected for study in TCGA. Data from TCGA (e.g., gene expression, copy number variation and clinical information), are available via the <a href="https://gdc.cancer.gov/" target="_blank">Genomic Data Commons (GDC)</a>. Data from TCGA projects are organized into two tiers: Open Access and Controlled Access. <ul> <li>Open Access data tier contains data that cannot be attributed to an individual research participant. The Open Access data tier does not require user certification. Data in Open Access tier are available in the TCGA Data Portal.</li> <li>Controlled Access data tier contains individual-level genotype data that are unique to an individual. Access to data in the Controlled Access data tier requires user certification through <a href="https://dbgap.ncbi.nlm.nih.gov/aa/wga.cgi?login=&page=login" target="_blank">dbGaP Authorized Access</a>.</li> <li>Controlled Access data types consist of the following: <ul> <li>Individual germline variant data (SNP .cel files)</li> <li>Primary sequence data (.bam files), which are available at GDC</li> <li>Clinical free text fields</li> <li>Exon Array files (for Glioblastoma and Ovarian projects only)</li> </ul> </li> </ul> NOTE: TCGA strives to release most data in the open access tier. Individual genotype or sequence files are prominent exceptions. Commonly requested files such as descriptions of somatic mutations or clinical data are open access. Please go to this page: <a href="https://tcga-data.nci.nih.gov/docs/publications/" target="_blank">https://tcga-data.nci.nih.gov/docs/publications/</a> to access all data associated with TCGA tumor specific publications. The TCGA study is utilized in the following dbGaP substudies. To view genotypes and other molecular data collected in these substudies, please click on the following substudies below or in the "Substudies" section of this top-level study page phs000178 TCGA study. <ul> <li><a href="./study.cgi?study_id=phs000854">phs000854</a> Genome-wide Analysis of Noncoding Regulatory Mutations in Cancer</li> </ul>

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Nassar2022 - Metagenomics Classification Task for Scientific Literature Text

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