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Title 

Prediction of cancer prognosis with the genetic basis of transcriptional variations

Authors 

Hyojung PaikE LeeI ParkJ KimD Lee

Publisher 

Elsevier

Issue Date 

2011

Citation 

Genomics, vol. 97, no. 6, pp. 350-357

Keywords 

Genetic architectureGenotypeSurvival predictionTranscriptional variation

Abstract 

Phenotypes of diseases, including prognosis, are likely to have complex etiologies and be derived from interactive mechanisms, including genetic and protein interactions. Many computational methods have been used to predict survival outcomes without explicitly identifying interactive effects, such as the genetic basis for transcriptional variations. We have therefore proposed a classification method based on the interaction between genotype and transcriptional expression features (CORE-F). This method considers the overall "genetic architecture," referring to genetically based transcriptional alterations that influence prognosis.In comparing the performance of CORE-F with the ensemble tree, the best-performing method predicting patient survival, we found that CORE-F outperformed the ensemble tree (mean AUC, 0.85 vs. 0.72). Moreover, the trained associations in the CORE-F successfully identified the genetic mechanisms underlying survival outcomes at the interaction-network level. Details of the learning algorithm are available in the online supplementary materials located at http://www.biosoft.kaist.ac.kr/coref.

ISSN 

0888-7543

Link 

http://dx.doi.org/10.1016/j.ygeno.2011.03.005

Appears in Collections

1. Journal Articles > Journal Articles

Registered Date

2019-05-02


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