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

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...

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Bibliographic Details
Main Authors: Kin Yau Wong, Cheng Fan, Maki Tanioka, Joel S. Parker, Andrew B. Nobel, Donglin Zeng, Dan-Yu Lin, Charles M. Perou
Format: Article
Language:English
Published: BMC 2019-03-01
Series:Genome Biology
Subjects:
Cancer genomics
Data integration
Gene modules
Variable selection
Online Access:http://link.springer.com/article/10.1186/s13059-019-1640-4

Internet

http://link.springer.com/article/10.1186/s13059-019-1640-4

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