bigbio/mirna

Citation text from the card

@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 m

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