upload hub_repos/mirna/README.md to hub from bigbio repo

README.md

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