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Title: I-Boost : an integrative boosting approach for predicting survival time with multiple genomics platforms
Authors: Wong, KY 
Fan, C
Tanioka, M
Parker, JS
Nobel, AB
Zeng, DL
Lin, DY
Perou, CM
Keywords: Cancer genomics
Data integration
Gene modules
Variable selection
Issue Date: 2019
Publisher: BioMed Central
Source: Genome biology, 7 Mar. 2019, v. 20, 52, p. 1-15 How to cite?
Journal: Genome biology 
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.
ISSN: 1474-7596
DOI: 10.1186/s13059-019-1640-4
Rights: © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (, which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to theCreative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver( applies to the data made available in this article, unless otherwise stated.
The following publication Wong, K. Y., Fan, C., Tanioka, M., Parker, J. S., Nobel, A. B., Zeng, D. L., ... & Perou, C. M. (2019). I-Boost: an integrative boosting approach for predicting survival time with multiple genomics platforms. Genome biology, 20(1), 52, 1-15 is available at
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