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--- |
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language: |
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- en |
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bigbio_language: |
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- English |
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license: cc-by-nc-3.0 |
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multilinguality: monolingual |
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bigbio_license_shortname: CC_BY_NC_3p0 |
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pretty_name: miRNA |
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homepage: https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/download-mirna-test-corpus.html |
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bigbio_pubmed: True |
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bigbio_public: True |
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bigbio_tasks: |
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- NAMED_ENTITY_RECOGNITION |
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- NAMED_ENTITY_DISAMBIGUATION |
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--- |
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# Dataset Card for miRNA |
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## Dataset Description |
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- **Homepage:** https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/download-mirna-test-corpus.html |
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- **Pubmed:** True |
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- **Public:** True |
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- **Tasks:** NER,NED |
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The corpus consists of 301 Medline citations. The documents were screened for |
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mentions of miRNA in the abstract text. Gene, disease and miRNA entities were manually |
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annotated. The corpus comprises of two separate files, a train and a test set, coming |
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from 201 and 100 documents respectively. |
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## Citation Information |
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``` |
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@Article{Bagewadi2014, |
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author={Bagewadi, Shweta |
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and Bobi{'{c}}, Tamara |
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and Hofmann-Apitius, Martin |
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and Fluck, Juliane |
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and Klinger, Roman}, |
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title={Detecting miRNA Mentions and Relations in Biomedical Literature}, |
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journal={F1000Research}, |
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year={2014}, |
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month={Aug}, |
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day={28}, |
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publisher={F1000Research}, |
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volume={3}, |
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pages={205-205}, |
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keywords={MicroRNAs; corpus; prediction algorithms}, |
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abstract={ |
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INTRODUCTION: MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional |
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gene expression regulators, participating in a wide spectrum of regulatory events such as |
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apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal |
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physiology, their dysregulation is implicated in a vast array of diseases. Dissection of |
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miRNA-related associations are valuable for contemplating their mechanism in diseases, |
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leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy. |
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MOTIVATION: Apart from databases and prediction tools, miRNA-related information is largely |
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available as unstructured text. Manual retrieval of these associations can be labor-intensive |
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due to steadily growing number of publications. Additionally, most of the published miRNA |
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entity recognition methods are keyword based, further subjected to manual inspection for |
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retrieval of relations. Despite the fact that several databases host miRNA-associations |
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derived from text, lower sensitivity and lack of published details for miRNA entity |
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recognition and associated relations identification has motivated the need for developing |
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comprehensive methods that are freely available for the scientific community. Additionally, |
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the lack of a standard corpus for miRNA-relations has caused difficulty in evaluating the |
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available systems. We propose methods to automatically extract mentions of miRNAs, species, |
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genes/proteins, disease, and relations from scientific literature. Our generated corpora, |
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along with dictionaries, and miRNA regular expression are freely available for academic |
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purposes. To our knowledge, these resources are the most comprehensive developed so far. |
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RESULTS: The identification of specific miRNA mentions reaches a recall of 0.94 and |
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precision of 0.93. Extraction of miRNA-disease and miRNA-gene relations lead to an |
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F1 score of up to 0.76. A comparison of the information extracted by our approach to |
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the databases miR2Disease and miRSel for the extraction of Alzheimer's disease |
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related relations shows the capability of our proposed methods in identifying correct |
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relations with improved sensitivity. The published resources and described methods can |
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help the researchers for maximal retrieval of miRNA-relations and generation of |
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miRNA-regulatory networks. AVAILABILITY: The training and test corpora, annotation |
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guidelines, developed dictionaries, and supplementary files are available at |
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http://www.scai.fraunhofer.de/mirna-corpora.html. |
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}, |
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note={26535109[pmid]}, |
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note={PMC4602280[pmcid]}, |
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issn={2046-1402}, |
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url={https://pubmed.ncbi.nlm.nih.gov/26535109}, |
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language={eng} |
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} |
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|
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``` |
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