title: Prediction of cancer prognosis with the genetic basis of transcriptional variations.
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.
authors.affiliation: Department of Bio and Brain Engineering, KAIST, Gwahangno, Yuseong-gu, Daejeon, Republic of Korea.
authors.forename: Hyojung
authors.initials: H
authors.lastname: Paik
authors.forename: Eunjung
authors.initials: E
authors.lastname: Lee
authors.forename: Inho
authors.initials: I
authors.lastname: Park
authors.forename: Junho
authors.initials: J
authors.lastname: Kim
authors.forename: Doheon
authors.initials: D
authors.lastname: Lee
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003198:Computer Simulation; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D056726:Genetic Association Studies; D006801:Humans; D053208:Kaplan-Meier Estimate; D008875:Middle Aged; D008957:Models, Genetic; D010051:Ovarian Neoplasms / Q000175:diagnosis / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide; D011379:Prognosis; D012372:ROC Curve; D014158:Transcription, Genetic