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


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.

SUBMITTER: Wong KY 

PROVIDER: S-EPMC6404283 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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

Wong Kin Yau KY   Fan Cheng C   Tanioka Maki M   Parker Joel S JS   Nobel Andrew B AB   Zeng Donglin D   Lin Dan-Yu DY   Perou Charles M CM  

Genome biology 20190307 1


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 typicall  ...[more]

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