Stable Feature Selection for Biomarker Discovery

TL;DR

This paper reviews stable feature selection methods for biomarker discovery, emphasizing the importance of stability against sampling variations and providing a comprehensive framework for categorization and future research.

Contribution

It offers a hierarchical framework to categorize existing stable feature selection methods, facilitating understanding and guiding future developments in biomarker discovery.

Findings

Provides an overview of stable feature selection techniques

Categorizes methods within a hierarchical framework

Highlights the importance of stability in biomarker discovery

Abstract

Feature selection techniques have been used as the workhorse in biomarker discovery applications for a long time. Surprisingly, the stability of feature selection with respect to sampling variations has long been under-considered. It is only until recently that this issue has received more and more attention. In this article, we review existing stable feature selection methods for biomarker discovery using a generic hierarchal framework. We have two objectives: (1) providing an overview on this new yet fast growing topic for a convenient reference; (2) categorizing existing methods under an expandable framework for future research and development.