NDLI: An ontology-based MicroRNA knowledge sharing and acquisition framework

Content Provider	IEEE Xplore Digital Library
Author	Jingshan Huang Xingyu Lu Jiangbo Dang Dejing Dou Blake, J.A. Gerthoffer, W.T. Ming Tan
Copyright Year	2012
Description	Author affiliation: Department of Biochemistry & Molecular Biology, University of South Alabama, Mobile, AL, U.S.A. (Dejing Dou) \|\| School of Computing University of South Alabama, Mobile, AL, U.S.A. (Jingshan Huang; Xingyu Lu) \|\| Computer and Information Science Department, University of Oregon, Eugene, OR, U.S.A. (Jiangbo Dang) \|\| Mitchell Cancer Institute, University of South Alabama, Mobile, AL, U.S.A. (Ming Tan) \|\| Siemens Corporate Research, Siemens Corporation, Princeton, NJ, U.S.A. (Blake, J.A.) \|\| The Jackson Laboratory, Bar Harbor, ME, U.S.A. (Gerthoffer, W.T.)
Abstract	MicroRNAs (miRNAs) play important roles in various biological processes by regulating their target genes. Therefore, miRNAs are closely associated with development, diagnosis, and prognosis for many diseases. The prediction of miRNA targets remains a challenging task for biologists because it involves an extremely large amount of data sources to be explored: to manually integrate information of identified targets and related information from various sources is time-consuming and error-prone; most of all, it is subject to biologists' limited prior knowledge. In this paper we investigated an ontology-based knowledge sharing framework to assist biologists in unraveling important roles of miRNAs in human disease in an automated and more efficient manner, (i) We developed the very first domain-specific ontologies in the miRNA field, Ontology for MicroRNA Target (OMIT), (ii) According to the global metadata model defined in ontologies, heterogeneous data sources were annotated and seamlessly integrated and stored into a central Resource Description Framework (RDF) data repository, (iii) We then enabled ontology-based queries, instead of traditional SQL queries, by inferring new statements from RDF data triples. Consequently we were able to acquire hidden knowledge originally implicit and unclear, yet critical, to biologists.
Starting Page	740
Ending Page	747
File Size	3672274
Page Count	8
File Format	PDF
ISBN	9781467327466
e-ISBN	9781467327473
e-ISBN	9781467327442
DOI	10.1109/BIBMW.2012.6470228
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Databases logic reasoning Semantics knowledge acquisition Ontologies semantic annotation and data integration miRNA Biology Resource description framework Cognition Engines ontology
Content Type	Text
Resource Type	Article

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