Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer Detection

TL;DR

This paper introduces a meta-learning approach to improve lung cancer detection from gene expression data, effectively addressing small sample size issues and outperforming traditional methods.

Contribution

The study presents a novel meta-learning framework tailored for gene expression-based lung cancer prediction, demonstrating superior performance over conventional and transfer learning methods.

Findings

Meta-learning outperforms traditional classifiers on augmented datasets.

The approach shows improved accuracy with limited sample sizes.

Explainability analysis reveals distinct decision patterns of the meta-learning model.

Abstract

Gene expression profiles obtained through DNA microarray have proven successful in providing critical information for cancer detection classifiers. However, the limited number of samples in these datasets poses a challenge to employ complex methodologies such as deep neural networks for sophisticated analysis. To address this "small data" dilemma, Meta-Learning has been introduced as a solution to enhance the optimization of machine learning models by utilizing similar datasets, thereby facilitating a quicker adaptation to target datasets without the requirement of sufficient samples. In this study, we present a meta-learning-based approach for predicting lung cancer from gene expression profiles. We apply this framework to well-established deep learning methodologies and employ four distinct datasets for the meta-learning tasks, where one as the target dataset and the rest as source datasets. Our approach is evaluated against both traditional and deep learning methodologies, and the results show the superior performance of meta-learning on augmented source data compared to the baselines trained on single datasets. Moreover, we conduct the comparative analysis between meta-learning and transfer learning methodologies to highlight the efficiency of the proposed approach in addressing the challenges associated with limited sample sizes. Finally, we incorporate the explainability study to illustrate the distinctiveness of decisions made by meta-learning.