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Identification of Pharmacologically Tractable Protein Complexes in Cancer Using the R-Based Network Clustering and Visualization Program MCODER
Joint Authors
Kwon, Sungjin
Kim, Hyosil
Kim, Hyun Seok
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-06-13
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Current multiomics assay platforms facilitate systematic identification of functional entities that are mappable in a biological network, and computational methods that are better able to detect densely connected clusters of signals within a biological network are considered increasingly important.
One of the most famous algorithms for detecting network subclusters is Molecular Complex Detection (MCODE).
MCODE, however, is limited in simultaneous analyses of multiple, large-scale data sets, since it runs on the Cytoscape platform, which requires extensive computational resources and has limited coding flexibility.
In the present study, we implemented the MCODE algorithm in R programming language and developed a related package, which we called MCODER.
We found the MCODER package to be particularly useful in analyzing multiple omics data sets simultaneously within the R framework.
Thus, we applied MCODER to detect pharmacologically tractable protein-protein interactions selectively elevated in molecular subtypes of ovarian and colorectal tumors.
In doing so, we found that a single molecular subtype representing epithelial-mesenchymal transition in both cancer types exhibited enhanced production of the collagen-integrin protein complex.
These results suggest that tumors of this molecular subtype could be susceptible to pharmacological inhibition of integrin signaling.
American Psychological Association (APA)
Kwon, Sungjin& Kim, Hyosil& Kim, Hyun Seok. 2017. Identification of Pharmacologically Tractable Protein Complexes in Cancer Using the R-Based Network Clustering and Visualization Program MCODER. BioMed Research International،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1133590
Modern Language Association (MLA)
Kwon, Sungjin…[et al.]. Identification of Pharmacologically Tractable Protein Complexes in Cancer Using the R-Based Network Clustering and Visualization Program MCODER. BioMed Research International No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1133590
American Medical Association (AMA)
Kwon, Sungjin& Kim, Hyosil& Kim, Hyun Seok. Identification of Pharmacologically Tractable Protein Complexes in Cancer Using the R-Based Network Clustering and Visualization Program MCODER. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1133590
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1133590