Self-supervised learning on gene expression data

TL;DR

This paper explores the use of self-supervised learning techniques on bulk gene expression data to improve phenotype prediction, reducing reliance on labeled data and outperforming traditional supervised methods.

Contribution

It is the first study applying self-supervised learning to bulk RNA-Seq data for phenotype prediction, demonstrating its effectiveness and providing practical recommendations.

Findings

Self-supervised methods outperform traditional supervised models in phenotype prediction.

The methods effectively capture complex information from unlabeled gene expression data.

The study offers insights into strengths and limitations of different self-supervised approaches.

Abstract

Predicting phenotypes from gene expression data is a crucial task in biomedical research, enabling insights into disease mechanisms, drug responses, and personalized medicine. Traditional machine learning and deep learning rely on supervised learning, which requires large quantities of labeled data that are costly and time-consuming to obtain in the case of gene expression data. Self-supervised learning has recently emerged as a promising approach to overcome these limitations by extracting information directly from the structure of unlabeled data. In this study, we investigate the application of state-of-the-art self-supervised learning methods to bulk gene expression data for phenotype prediction. We selected three self-supervised methods, based on different approaches, to assess their ability to exploit the inherent structure of the data and to generate qualitative representations which can be used for downstream predictive tasks. By using several publicly available gene expression datasets, we demonstrate how the selected methods can effectively capture complex information and improve phenotype prediction accuracy. The results obtained show that self-supervised learning methods can outperform traditional supervised models besides offering significant advantage by reducing the dependency on annotated data. We provide a comprehensive analysis of the performance of each method by highlighting their strengths and limitations. We also provide recommendations for using these methods depending on the case under study. Finally, we outline future research directions to enhance the application of self-supervised learning in the field of gene expression data analysis. This study is the first work that deals with bulk RNA-Seq data and self-supervised learning.