Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이상혁 | * |
dc.date.accessioned | 2016-08-29T12:08:38Z | - |
dc.date.available | 2016-08-29T12:08:38Z | - |
dc.date.issued | 2016 | * |
dc.identifier.issn | 1745-6150 | * |
dc.identifier.other | OAK-18426 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/231461 | - |
dc.description.abstract | Background: 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.language | English | * |
dc.publisher | BioMed Central Ltd. | * |
dc.subject | Graph clustering | * |
dc.subject | Network modeling | * |
dc.subject | Network visualization | * |
dc.subject | Omics data analysis | * |
dc.subject | Over-representation analysis | * |
dc.title | MONGKIE: An integrated tool for network analysis and visualization for multi-omics data | * |
dc.type | Article | * |
dc.relation.issue | 1 | * |
dc.relation.volume | 11 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.journaltitle | Biology Direct | * |
dc.identifier.doi | 10.1186/s13062-016-0112-y | * |
dc.identifier.wosid | WOS:000372499900001 | * |
dc.identifier.scopusid | 2-s2.0-84962784481 | * |
dc.author.google | Jang Y. | * |
dc.author.google | Yu N. | * |
dc.author.google | Seo J. | * |
dc.author.google | Kim S. | * |
dc.author.google | Lee S. | * |
dc.contributor.scopusid | 이상혁(57212112170) | * |
dc.date.modifydate | 20240415122632 | * |