miRNA数据集数据卡片

该语料库由301篇Medline文献引用组成。这些文档被筛选出在摘要文本中提及miRNA的内容。基因、疾病和miRNA实体进行了手动标注。该语料库包含两个独立的文件，一个是由201个文档组成的训练集，另一个是由100个文档组成的测试集。

引用信息

@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}
}