Structured Detection of Interactions with the Directed Lasso

Structured Detection of Interactions with the Directed Lasso When considering low-dimensional gene–treatment or gene–environment interactions, we might suspect groups of genes to interact with treatment or environment in a similar way. For example, genes associated with related biological processes might interact with an environmental factor or a clinical treatment in its effect on a phenotype correspondingly. We use the idea of a structured interaction model together with penalized regression to limit the model complexity in a model in which we believe the interactions might behave in a similar way. We propose the directed lasso, a regression modeling strategy using a pairwise fused lasso penalty to encourage interaction model simplicity through fusion of effect size. We compare the performance of the directed lasso to the lasso and other methods in a simulation study and on data sampled from a breast cancer clinical trial. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistics in Biosciences Springer Journals

Structured Detection of Interactions with the Directed Lasso

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Publisher
Springer US
Copyright
Copyright © 2016 by International Chinese Statistical Association
Subject
Statistics; Statistics for Life Sciences, Medicine, Health Sciences; Biostatistics; Theoretical Ecology/Statistics
ISSN
1867-1764
eISSN
1867-1772
D.O.I.
10.1007/s12561-016-9184-6
Publisher site
See Article on Publisher Site

Abstract

When considering low-dimensional gene–treatment or gene–environment interactions, we might suspect groups of genes to interact with treatment or environment in a similar way. For example, genes associated with related biological processes might interact with an environmental factor or a clinical treatment in its effect on a phenotype correspondingly. We use the idea of a structured interaction model together with penalized regression to limit the model complexity in a model in which we believe the interactions might behave in a similar way. We propose the directed lasso, a regression modeling strategy using a pairwise fused lasso penalty to encourage interaction model simplicity through fusion of effect size. We compare the performance of the directed lasso to the lasso and other methods in a simulation study and on data sampled from a breast cancer clinical trial.

Journal

Statistics in BiosciencesSpringer Journals

Published: Nov 29, 2016

References

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