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

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. Emergi...

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Autores principales:	Jingshan Huang, Jiangbo Dang, Glen M Borchert, Karen Eilbeck, He Zhang, Min Xiong, Weijian Jiang, Hao Wu, Judith A Blake, Darren A Natale, Ming Tan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0bc4f754dbbe4cbbbb82e98af7d3cc28
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spelling	oai:doaj.org-article:0bc4f754dbbe4cbbbb82e98af7d3cc282021-11-25T06:08:32ZOMIT: dynamic, semi-automated ontology development for the microRNA domain.1932-620310.1371/journal.pone.0100855https://doaj.org/article/0bc4f754dbbe4cbbbb82e98af7d3cc282014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25025130/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203As 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.Jingshan HuangJiangbo DangGlen M BorchertKaren EilbeckHe ZhangMin XiongWeijian JiangHao WuJudith A BlakeDarren A NataleMing TanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 7, p e100855 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jingshan Huang Jiangbo Dang Glen M Borchert Karen Eilbeck He Zhang Min Xiong Weijian Jiang Hao Wu Judith A Blake Darren A Natale Ming Tan OMIT: dynamic, semi-automated ontology development for the microRNA domain.
description	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.
format	article
author	Jingshan Huang Jiangbo Dang Glen M Borchert Karen Eilbeck He Zhang Min Xiong Weijian Jiang Hao Wu Judith A Blake Darren A Natale Ming Tan
author_facet	Jingshan Huang Jiangbo Dang Glen M Borchert Karen Eilbeck He Zhang Min Xiong Weijian Jiang Hao Wu Judith A Blake Darren A Natale Ming Tan
author_sort	Jingshan Huang
title	OMIT: dynamic, semi-automated ontology development for the microRNA domain.
title_short	OMIT: dynamic, semi-automated ontology development for the microRNA domain.
title_full	OMIT: dynamic, semi-automated ontology development for the microRNA domain.
title_fullStr	OMIT: dynamic, semi-automated ontology development for the microRNA domain.
title_full_unstemmed	OMIT: dynamic, semi-automated ontology development for the microRNA domain.
title_sort	omit: dynamic, semi-automated ontology development for the microrna domain.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/0bc4f754dbbe4cbbbb82e98af7d3cc28
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OMIT: dynamic, semi-automated ontology development for the microRNA domain.

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