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

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number52
JournalGenome Biology
Volume20
Issue number1
DOIs
Publication statusPublished - 7 Mar 2019

Keywords

  • Cancer genomics
  • Data integration
  • Gene modules
  • Variable selection

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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