Prediction of cancer prognosis with the genetic basis of transcriptional variations

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Title
Prediction of cancer prognosis with the genetic basis of transcriptional variations
Author(s)
Hyojung Paik; E Lee; I Park; J Kim; D Lee
Bibliographic Citation
Genomics, vol. 97, no. 6, pp. 350-357
Publication Year
2011
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.
Keyword
Genetic architectureGenotypeSurvival predictionTranscriptional variation
ISSN
0888-7543
Publisher
Elsevier
DOI
http://dx.doi.org/10.1016/j.ygeno.2011.03.005
Type
Article
Appears in Collections:
1. Journal Articles > Journal Articles
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