Detecting miRNA Mentions and Relations in Biomedical Literature
#MMPMID26535109
Bagewadi S
; Bobi? T
; Hofmann-Apitius M
; Fluck J
; Klinger R
F1000Res
2014[]; 3
(?): 205
PMID26535109
show ga
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 F 1 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.
free
free
free
