A Paradigmatic Regression Algorithm for Gene Selection Problems

TL;DR

This paper introduces a novel regression-based algorithm for gene selection that optimizes a prediction-focused objective, enabling the identification of smaller, effective gene sets and providing multiple models for flexible prediction accuracy.

Contribution

It presents a new variable selection procedure resembling importance sampling, tailored for gene expression data, improving model size and interpretability over existing methods.

Findings

Smaller gene models with comparable or better accuracy.

The method outperforms competing approaches on cancer datasets.

Provides a network of models for different prediction accuracy levels.

Abstract

Motivation: Gene selection has become a common task in most gene expression studies. The objective of such research is often to identify the smallest possible set of genes that can still achieve good predictive performance. The problem of assigning tumours to a known class is a particularly important example that has received considerable attention in the last ten years. Many of the classification methods proposed recently require some form of dimension-reduction of the problem. These methods provide a single model as an output and, in most cases, rely on the likelihood function in order to achieve variable selection. Results: We propose a prediction-based objective function that can be tailored to the requirements of practitioners and can be used to assess and interpret a given problem. The direct optimization of such a function can be very difficult because the problem is potentially discontinuous and nonconvex. We therefore propose a general procedure for variable selection that resembles importance sampling to explore the feature space. Our proposal compares favorably with competing alternatives when applied to two cancer data sets in that smaller models are obtained for better or at least comparable classification errors. Furthermore by providing a set of selected models instead of a single one, we construct a network of possible models for a target prediction accuracy level.