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dc.contributor.author이상혁-
dc.date.accessioned2017-08-29T05:05:41Z-
dc.date.available2017-08-29T05:05:41Z-
dc.date.issued2016-
dc.identifier.issn1745-6150-
dc.identifier.otherOAK-18426-
dc.identifier.urihttp://dspace.ewha.ac.kr/handle/2015.oak/231461-
dc.description.abstractBackground: Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed. Results: Here, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers. Conclusion: We believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases. Reviewers: This article was reviewed by Prof. Limsoon Wong, Prof. Soojin Yi, and Maciej M Kańduła (nominated by Prof. David P Kreil). © 2016 Jang et al.-
dc.languageEnglish-
dc.publisherBioMed Central Ltd.-
dc.subjectGraph clustering-
dc.subjectNetwork modeling-
dc.subjectNetwork visualization-
dc.subjectOmics data analysis-
dc.subjectOver-representation analysis-
dc.titleMONGKIE: An integrated tool for network analysis and visualization for multi-omics data-
dc.typeArticle-
dc.relation.issue1-
dc.relation.volume11-
dc.relation.indexSCIE-
dc.relation.indexSCOPUS-
dc.relation.journaltitleBiology Direct-
dc.identifier.doi10.1186/s13062-016-0112-y-
dc.identifier.wosidWOS:000372499900001-
dc.identifier.scopusid2-s2.0-84962784481-
dc.author.googleJang Y.-
dc.author.googleYu N.-
dc.author.googleSeo J.-
dc.author.googleKim S.-
dc.author.googleLee S.-
dc.contributor.scopusid이상혁(55716450000)-
dc.date.modifydate20190901081003-


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