Cancer Transcriptome Dataset Analysis: Comparing Methods of Pathway and Gene Regulatory Network-Based Cluster Identification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nam, Seungyoon | - |
dc.date.available | 2020-02-27T19:41:32Z | - |
dc.date.created | 2020-02-06 | - |
dc.date.issued | 2017-04 | - |
dc.identifier.issn | 1536-2310 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/6265 | - |
dc.description.abstract | Cancer transcriptome analysis is one of the leading areas of Big Data science, biomarker, and pharmaceutical discovery, not to forget personalized medicine. Yet, cancer transcriptomics and postgenomic medicine require innovation in bioinformatics as well as comparison of the performance of available algorithms. In this data analytics context, the value of network generation and algorithms has been widely underscored for addressing the salient questions in cancer pathogenesis. Analysis of cancer trancriptome often results in complicated networks where identification of network modularity remains critical, for example, in delineating the druggable molecular targets. Network clustering is useful, but depends on the network topology in and of itself. Notably, the performance of different network-generating tools for network cluster (NC) identification has been little investigated to date. Hence, using gastric cancer (GC) transcriptomic datasets, we compared two algorithms for generating pathway versus gene regulatory network-based NCs, showing that the pathway-based approach better agrees with a reference set of cancer-functional contexts. Finally, by applying pathway-based NC identification to GC transcriptome datasets, we describe cancer NCs that associate with candidate therapeutic targets and biomarkers in GC. These observations collectively inform future research on cancer transcriptomics, drug discovery, and rational development of new analysis tools for optimal harnessing of omics data. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MARY ANN LIEBERT, INC | - |
dc.relation.isPartOf | OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY | - |
dc.subject | SPLEEN TYROSINE KINASE | - |
dc.subject | COLORECTAL-CANCER | - |
dc.subject | INTEGRATIVE ANALYSIS | - |
dc.subject | BREAST-CANCER | - |
dc.subject | C-MET | - |
dc.subject | EGFR | - |
dc.subject | ALGORITHM | - |
dc.subject | RESISTANCE | - |
dc.subject | INHIBITION | - |
dc.subject | EXPRESSION | - |
dc.title | Cancer Transcriptome Dataset Analysis: Comparing Methods of Pathway and Gene Regulatory Network-Based Cluster Identification | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000398819600004 | - |
dc.identifier.doi | 10.1089/omi.2016.0169 | - |
dc.identifier.bibliographicCitation | OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY, v.21, no.4, pp.217 - 224 | - |
dc.identifier.scopusid | 2-s2.0-85017310501 | - |
dc.citation.endPage | 224 | - |
dc.citation.startPage | 217 | - |
dc.citation.title | OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY | - |
dc.citation.volume | 21 | - |
dc.citation.number | 4 | - |
dc.contributor.affiliatedAuthor | Nam, Seungyoon | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | cancer transcriptomics | - |
dc.subject.keywordAuthor | computational biology | - |
dc.subject.keywordAuthor | gastric cancer | - |
dc.subject.keywordAuthor | genomics | - |
dc.subject.keywordAuthor | systems biology | - |
dc.subject.keywordPlus | SPLEEN TYROSINE KINASE | - |
dc.subject.keywordPlus | COLORECTAL-CANCER | - |
dc.subject.keywordPlus | INTEGRATIVE ANALYSIS | - |
dc.subject.keywordPlus | BREAST-CANCER | - |
dc.subject.keywordPlus | C-MET | - |
dc.subject.keywordPlus | EGFR | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | RESISTANCE | - |
dc.subject.keywordPlus | INHIBITION | - |
dc.subject.keywordPlus | EXPRESSION | - |
dc.relation.journalResearchArea | Biotechnology & Applied Microbiology | - |
dc.relation.journalResearchArea | Genetics & Heredity | - |
dc.relation.journalWebOfScienceCategory | Biotechnology & Applied Microbiology | - |
dc.relation.journalWebOfScienceCategory | Genetics & Heredity | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.