APPEX: analysis platform for the identification of prognostic gene expression signatures in cancer

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Title
APPEX: analysis platform for the identification of prognostic gene expression signatures in cancer
Author(s)
Seon-Kyu Kim; Jong-Hwan Kim; S J Yoon; W J Kim; Seon-Young Kim
Bibliographic Citation
Bioinformatics, vol. 30, no. 22, pp. 3284-3286
Publication Year
2014
Abstract
Because cancer has heterogeneous clinical behaviors due to the progressive accumulation of multiple genetic and epigenetic alterations, the identification of robust molecular signatures for predicting cancer outcome is profoundly important. Here, we introduce the APPEX Web-based analysis platform as a versatile tool for identifying prognostic molecular signatures that predict cancer diversity. We incorporated most of statistical methods for survival analysis and implemented seven survival analysis workflows, including CoxSingle, CoxMulti, IntransSingle, IntransMulti, SuperPC, TimeRoc and multivariate. A total of 236 publicly available datasets were collected, processed and stored to support easy independent validation of prognostic signatures. Two case studies including disease recurrence and bladder cancer progression were described using different combinations of the seven workflows.
ISSN
1367-4803
Publisher
Oxford Univ Press
DOI
http://dx.doi.org/10.1093/bioinformatics/btu521
Type
Article
Appears in Collections:
Division of Biomedical Research > Personalized Genomic Medicine Research Center > 1. Journal Articles
Korea Bioinformation Center > 1. Journal Articles
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