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PLoS One. 2014 Jul 15;9(7):e100855. doi: 10.1371/journal.pone.0100855. eCollection 2014.

OMIT: dynamic, semi-automated ontology development for the microRNA domain.

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School of Computing, University of South Alabama, Mobile, Alabama, United States of America.
Corporate Technology, Siemens Corporation, Princeton, New Jersey, United States of America.
Department of Biology, University of South Alabama, Mobile, Alabama, United States of America.
School of Medicine, University of Utah, Salt Lake City, Utah, United States of America.
The Jackson Laboratory, Bar Harbor, Maine, United States of America.
Georgetown University Medical Center, Washington, DC, United States of America.
Mitchell Cancer Institute, University of South Alabama, Mobile, Alabama, United States of America.


As a special class of short non-coding RNAs, microRNAs (a.k.a. miRNAs or miRs) have been reported to perform important roles in various biological processes by regulating respective target genes. However, significant barriers exist during biologists' conventional miR knowledge discovery. Emerging semantic technologies, which are based upon domain ontologies, can render critical assistance to this problem. Our previous research has investigated the construction of a miR ontology, named Ontology for MIcroRNA Target Prediction (OMIT), the very first of its kind that formally encodes miR domain knowledge. Although it is unavoidable to have a manual component contributed by domain experts when building ontologies, many challenges have been identified for a completely manual development process. The most significant issue is that a manual development process is very labor-intensive and thus extremely expensive. Therefore, we propose in this paper an innovative ontology development methodology. Our contributions can be summarized as: (i) We have continued the development and critical improvement of OMIT, solidly based on our previous research outcomes. (ii) We have explored effective and efficient algorithms with which the ontology development can be seamlessly combined with machine intelligence and be accomplished in a semi-automated manner, thus significantly reducing large amounts of human efforts. A set of experiments have been conducted to thoroughly evaluate our proposed methodology.

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