Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

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

Authors
Lee, JusangJo, KyuriLee, SunwonKang, JaewooKim, Sun
Issue Date
23-12월-2016
Publisher
BMC
Keywords
Literature information; Pathway; Pathway analysis; Pathway prioritization; Time series
Citation
BMC BIOINFORMATICS, v.17
Indexed
SCIE
SCOPUS
Journal Title
BMC BIOINFORMATICS
Volume
17
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/86502
DOI
10.1186/s12859-016-1335-8
ISSN
1471-2105
Abstract
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.
Files in This Item
There are no files associated with this item.
Appears in
Collections
Graduate School > Department of Computer Science and Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Kang, Jae woo photo

Kang, Jae woo
컴퓨터학과
Read more

Altmetrics

Total Views & Downloads

BROWSE