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Title: I-Boost: an integrative boosting approach for predicting survival time with multiple genomics platforms.

Authors: Wong, Kin Yau; Fan, Cheng; Tanioka, Maki; Parker, Joel S; Nobel, Andrew B; Zeng, Donglin; Lin, Dan-Yu; Perou, Charles M

Published In Genome Biol, (2019 Mar 07)

Abstract: We propose a statistical boosting method, termed I-Boost, to integrate multiple types of high-dimensional genomics data with clinical data for predicting survival time. I-Boost provides substantially higher prediction accuracy than existing methods. By applying I-Boost to The Cancer Genome Atlas, we show that the integration of multiple genomics platforms with clinical variables improves the prediction of survival time over the use of clinical variables alone; gene expression values are typically more prognostic of survival time than other genomics data types; and gene modules/signatures are at least as prognostic as the collection of individual gene expression data.

PubMed ID: 30845957 Exiting the NIEHS site

MeSH Terms: Gene Expression Profiling/methods*; Gene Expression Regulation, Neoplastic*; Gene Regulatory Networks*; Genomics/methods*; Humans; Models, Statistical; Neoplasms/genetics; Neoplasms/mortality*; Prognosis; Software*; Survival Rate

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