AUTHOR=Luo Xihaier , Niyakan Seyednami , Johnstone Patrick , McCorkle Sean , Park Gilchan , López-Marrero Vanessa , Yoo Shinjae , Dougherty Edward R. , Qian Xiaoning , Alexander Francis J. , Jha Shantenu , Yoon Byung-Jun TITLE=Pathway-based analyses of gene expression profiles at low doses of ionizing radiation JOURNAL=Frontiers in Bioinformatics VOLUME=4 YEAR=2024 URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2024.1280971 DOI=10.3389/fbinf.2024.1280971 ISSN=2673-7647 ABSTRACT=

Radiation exposure poses a significant threat to human health. Emerging research indicates that even low-dose radiation once believed to be safe, may have harmful effects. This perception has spurred a growing interest in investigating the potential risks associated with low-dose radiation exposure across various scenarios. To comprehensively explore the health consequences of low-dose radiation, our study employs a robust statistical framework that examines whether specific groups of genes, belonging to known pathways, exhibit coordinated expression patterns that align with the radiation levels. Notably, our findings reveal the existence of intricate yet consistent signatures that reflect the molecular response to radiation exposure, distinguishing between low-dose and high-dose radiation. Moreover, we leverage a pathway-constrained variational autoencoder to capture the nonlinear interactions within gene expression data. By comparing these two analytical approaches, our study aims to gain valuable insights into the impact of low-dose radiation on gene expression patterns, identify pathways that are differentially affected, and harness the potential of machine learning to uncover hidden activity within biological networks. This comparative analysis contributes to a deeper understanding of the molecular consequences of low-dose radiation exposure.