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Prioritizing biological pathways by recognizing context in time-series gene expression data

DC Field Value Language
dc.contributor.authorLee, Jusang-
dc.contributor.authorJo, Kyuri-
dc.contributor.authorLee, Sunwon-
dc.contributor.authorKang, Jaewoo-
dc.contributor.authorKim, Sun-
dc.date.accessioned2017-02-06T04:16:00Z-
dc.date.available2017-02-06T04:16:00Z-
dc.date.issued2016-12-23-
dc.identifier.citationBMC Bioinformatics, 17(Suppl 17):477ko_KR
dc.identifier.urihttps://hdl.handle.net/10371/100438-
dc.descriptionThis article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
ko_KR
dc.description.abstractAbstract

Background
The primary goal of pathway analysis using transcriptome data is to find significantly perturbed pathways. However, pathway analysis is not always successful in identifying pathways that are truly relevant to the context under study. A major reason for this difficulty is that a single gene is involved in multiple pathways. In the KEGG pathway database, there are 146 genes, each of which is involved in more than 20 pathways. Thus activation of even a single gene will result in activation of many pathways. This complex relationship often makes the pathway analysis very difficult. While we need much more powerful pathway analysis methods, a readily available alternative way is to incorporate the literature information.


Results
In this study, we propose a novel approach for prioritizing pathways by combining results from both pathway analysis tools and literature information. The basic idea is as follows. Whenever there are enough articles that provide evidence on which pathways are relevant to the context, we can be assured that the pathways are indeed related to the context, which is termed as relevance in this paper. However, if there are few or no articles reported, then we should rely on the results from the pathway analysis tools, which is termed as significance in this paper. We realized this concept as an algorithm by introducing Context Score and Impact Score and then combining the two into a single score. Our method ranked truly relevant pathways significantly higher than existing pathway analysis tools in experiments with two data sets.


Conclusions
Our novel framework was implemented as ContextTRAP by utilizing two existing tools, TRAP and BEST. ContextTRAP will be a useful tool for the pathway based analysis of gene expression data since the user can specify the context of the biological experiment in a set of keywords. The web version of ContextTRAP is available at
http://biohealth.snu.ac.kr/software/contextTRAP

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ko_KR
dc.language.isoenko_KR
dc.publisherBioMed Centralko_KR
dc.subjectLiterature informationko_KR
dc.subjectPathwayko_KR
dc.subjectPathway analysisko_KR
dc.subjectPathway prioritizationko_KR
dc.subjectTime seriesko_KR
dc.titlePrioritizing biological pathways by recognizing context in time-series gene expression datako_KR
dc.typeArticleko_KR
dc.contributor.AlternativeAuthor이주상-
dc.contributor.AlternativeAuthor조규리-
dc.contributor.AlternativeAuthor이순원-
dc.contributor.AlternativeAuthor강재우-
dc.contributor.AlternativeAuthor김선-
dc.identifier.doi10.1186/s12859-016-1335-8-
dc.language.rfc3066en-
dc.rights.holderThe Author(s)-
dc.date.updated2017-01-06T10:00:25Z-
Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Electrical and Computer Engineering (전기·정보공학부)Journal Papers (저널논문_전기·정보공학부)
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