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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido


IMPACT FACTOR 2018: 0.536
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CiteScore 2018: 0.49

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Source Normalized Impact per Paper (SNIP) 2018: 0.342

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Online
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1544-6115
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Volume 9, Issue 1

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Buckley-James Boosting for Survival Analysis with High-Dimensional Biomarker Data

Zhu Wang / C.Y. Wang
Published Online: 2010-06-08 | DOI: https://doi.org/10.2202/1544-6115.1550

There has been increasing interest in predicting patients' survival after therapy by investigating gene expression microarray data. In the regression and classification models with high-dimensional genomic data, boosting has been successfully applied to build accurate predictive models and conduct variable selection simultaneously. We propose the Buckley-James boosting for the semiparametric accelerated failure time models with right censored survival data, which can be used to predict survival of future patients using the high-dimensional genomic data. In the spirit of adaptive LASSO, twin boosting is also incorporated to fit more sparse models. The proposed methods have a unified approach to fit linear models, non-linear effects models with possible interactions. The methods can perform variable selection and parameter estimation simultaneously. The proposed methods are evaluated by simulations and applied to a recent microarray gene expression data set for patients with diffuse large B-cell lymphoma under the current gold standard therapy.

Keywords: boosting; accelerated failure time model; Buckley-James estimator; censored survival data; LASSO; variable selection

About the article

Published Online: 2010-06-08


Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 9, Issue 1, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1550.

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