{
 "id": "bigbio/mirna",
 "owner": "bigbio",
 "name": "mirna",
 "downloads": 55,
 "likes": 1,
 "last_modified": "2022-12-22T15:45:38.000Z",
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 "citation": "@Article{Bagewadi2014,\nauthor={Bagewadi, Shweta\nand Bobi{'{c}}, Tamara\nand Hofmann-Apitius, Martin\nand Fluck, Juliane\nand Klinger, Roman},\ntitle={Detecting miRNA Mentions and Relations in Biomedical Literature},\njournal={F1000Research},\nyear={2014},\nmonth={Aug},\nday={28},\npublisher={F1000Research},\nvolume={3},\npages={205-205},\nkeywords={MicroRNAs; corpus; prediction algorithms},\nabstract={\n    INTRODUCTION: MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional\n    gene expression regulators, participating in a wide spectrum of regulatory events such as\n    apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal\n    physiology, their dysregulation is implicated in a vast array of diseases. Dissection of\n    miRNA-related associations are valuable for contemplating their mechanism in diseases,\n    leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy.\n    MOTIVATION: Apart from databases and prediction tools, miRNA-related information is largely\n    available as unstructured text. Manual retrieval of these associations can be labor-intensive\n    due to steadily growing number of publications. Additionally, most of the published miRNA\n    entity recognition methods are keyword based, further subjected to manual inspection for\n    retrieval of relations. Despite the fact that several databases host miRNA-associations\n    derived from text, lower sensitivity and lack of published details for m",
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}