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J Biomed Semantics. 2016 Apr 29;7(1):9. doi: 10.1186/s13326-015-0044-y.

miRiaD: A Text Mining Tool for Detecting Associations of microRNAs with Diseases.

Author information

1
Department of Computer and Information Sciences, University of Delaware, Newark, DE, 19711, USA. sgupta@udel.edu.
2
Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, 19711, USA.
3
Department of Computer and Information Sciences, University of Delaware, Newark, DE, 19711, USA.
4
Department of Food and Animal Sciences, University of Delaware, Newark, DE, 19711, USA.

Abstract

BACKGROUND:

MicroRNAs are increasingly being appreciated as critical players in human diseases, and questions concerning the role of microRNAs arise in many areas of biomedical research. There are several manually curated databases of microRNA-disease associations gathered from the biomedical literature; however, it is difficult for curators of these databases to keep up with the explosion of publications in the microRNA-disease field. Moreover, automated literature mining tools that assist manual curation of microRNA-disease associations currently capture only one microRNA property (expression) in the context of one disease (cancer). Thus, there is a clear need to develop more sophisticated automated literature mining tools that capture a variety of microRNA properties and relations in the context of multiple diseases to provide researchers with fast access to the most recent published information and to streamline and accelerate manual curation.

METHODS:

We have developed miRiaD (microRNAs in association with Disease), a text-mining tool that automatically extracts associations between microRNAs and diseases from the literature. These associations are often not directly linked, and the intermediate relations are often highly informative for the biomedical researcher. Thus, miRiaD extracts the miR-disease pairs together with an explanation for their association. We also developed a procedure that assigns scores to sentences, marking their informativeness, based on the microRNA-disease relation observed within the sentence.

RESULTS:

miRiaD was applied to the entire Medline corpus, identifying 8301 PMIDs with miR-disease associations. These abstracts and the miR-disease associations are available for browsing at http://biotm.cis.udel.edu/miRiaD . We evaluated the recall and precision of miRiaD with respect to information of high interest to public microRNA-disease database curators (expression and target gene associations), obtaining a recall of 88.46-90.78. When we expanded the evaluation to include sentences with a wide range of microRNA-disease information that may be of interest to biomedical researchers, miRiaD also performed very well with a F-score of 89.4. The informativeness ranking of sentences was evaluated in terms of nDCG (0.977) and correlation metrics (0.678-0.727) when compared to an annotator's ranked list.

CONCLUSIONS:

miRiaD, a high performance system that can capture a wide variety of microRNA-disease related information, extends beyond the scope of existing microRNA-disease resources. It can be incorporated into manual curation pipelines and serve as a resource for biomedical researchers interested in the role of microRNAs in disease. In our ongoing work we are developing an improved miRiaD web interface that will facilitate complex queries about microRNA-disease relationships, such as "In what diseases does microRNA regulation of apoptosis play a role?" or "Is there overlap in the sets of genes targeted by microRNAs in different types of dementia?"."

KEYWORDS:

Associations; Disease; MicroRNA; Natural language processing; Relation extraction; Text-mining

PMID:
27216254
PMCID:
PMC4877743
DOI:
10.1186/s13326-015-0044-y
[Indexed for MEDLINE]
Free PMC Article

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