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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Simultaneous grouping pursuit and feature selection over an undirected graph.

Yunzhang Zhu, Xiaotong Shen, Wei Pan

Journal of the American Statistical Association

|October 8, 2013

Summary

This study introduces a novel method for simultaneous grouping and feature selection in high-dimensional regression, improving model parsimony and predictive accuracy. The approach effectively identifies relevant predictors and their groupings, even with complex network structures.

Keywords:

Network analysis nonconvex minimization prediction structured data

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Area of Science:

Statistics
Bioinformatics
Machine Learning

Background:

High-dimensional regression faces challenges like the curse of dimensionality.
Grouping pursuit and feature selection are crucial for parsimonious model building.
Gene network analysis highlights the importance of grouped biological functionalities.

Purpose of the Study:

To develop a method for simultaneous grouping pursuit and feature selection in high-dimensional regression.
To incorporate arbitrary undirected graph structures representing predictor relationships.
To achieve a parsimonious model by grouping correlated predictors.

Main Methods:

Simultaneous grouping pursuit and feature selection using a nonconvex penalty.
Development of a computational strategy for the proposed method.
Theoretical analysis to demonstrate oracle estimator reconstruction.

Main Results:

The method consistently reconstructs grouping structures and identifies informative features.
Achieves optimal parameter estimation.
Demonstrates superior selection accuracy and predictive performance compared to existing methods in simulations.

Conclusions:

The proposed method effectively integrates grouping pursuit and feature selection.
It offers a powerful tool for analyzing high-dimensional data with network structures, such as in gene expression quantitative trait loci (eQTL) analysis.