Sparse regularized discriminant analysis with application to microarrays

Ran Li; Baolin Wu

doi:10.1016/j.compbiolchem.2012.06.001

Sparse regularized discriminant analysis with application to microarrays

Ran Li, Baolin Wu

Biostatistics

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

For cancer prediction using large-scale gene expression data, it often helps to incorporate gene interactions in the model. However it is not straightforward to simultaneously select important genes while modeling gene interactions. Some heuristic approaches have been proposed in the literature. In this paper, we study a unified modeling approach based on the ℓ₁ penalized likelihood estimation that can simultaneously select important genes and model gene interactions. We will illustrate its competitive performance through simulation studies and applications to public microarray data.

Original language	English (US)
Pages (from-to)	14-19
Number of pages	6
Journal	Computational Biology and Chemistry
Volume	39
DOIs	https://doi.org/10.1016/j.compbiolchem.2012.06.001
State	Published - Aug 2012

Bibliographical note

Funding Information:
This research was supported in part by NIH grant GM083345 and CA134848 . We would like to thank two anonymous referees for their constructive comments that have dramatically improved the presentation of the paper.

Keywords

Lasso
Microarray data
PCA
Prediction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.compbiolchem.2012.06.001

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abstract = "For cancer prediction using large-scale gene expression data, it often helps to incorporate gene interactions in the model. However it is not straightforward to simultaneously select important genes while modeling gene interactions. Some heuristic approaches have been proposed in the literature. In this paper, we study a unified modeling approach based on the ℓ1 penalized likelihood estimation that can simultaneously select important genes and model gene interactions. We will illustrate its competitive performance through simulation studies and applications to public microarray data.",

keywords = "Lasso, Microarray data, PCA, Prediction",

author = "Ran Li and Baolin Wu",

note = "Funding Information: This research was supported in part by NIH grant GM083345 and CA134848 . We would like to thank two anonymous referees for their constructive comments that have dramatically improved the presentation of the paper. ",

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AU - Wu, Baolin

N1 - Funding Information: This research was supported in part by NIH grant GM083345 and CA134848 . We would like to thank two anonymous referees for their constructive comments that have dramatically improved the presentation of the paper.

PY - 2012/8

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N2 - For cancer prediction using large-scale gene expression data, it often helps to incorporate gene interactions in the model. However it is not straightforward to simultaneously select important genes while modeling gene interactions. Some heuristic approaches have been proposed in the literature. In this paper, we study a unified modeling approach based on the ℓ1 penalized likelihood estimation that can simultaneously select important genes and model gene interactions. We will illustrate its competitive performance through simulation studies and applications to public microarray data.

AB - For cancer prediction using large-scale gene expression data, it often helps to incorporate gene interactions in the model. However it is not straightforward to simultaneously select important genes while modeling gene interactions. Some heuristic approaches have been proposed in the literature. In this paper, we study a unified modeling approach based on the ℓ1 penalized likelihood estimation that can simultaneously select important genes and model gene interactions. We will illustrate its competitive performance through simulation studies and applications to public microarray data.

KW - Lasso

KW - Microarray data

KW - PCA

KW - Prediction

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SN - 1476-9271

VL - 39

SP - 14

EP - 19

JO - Computational Biology and Chemistry

JF - Computational Biology and Chemistry

ER -

Sparse regularized discriminant analysis with application to microarrays

Abstract

Bibliographical note

Keywords

UN SDGs

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