Gene Expression Data

Gene expression data analysis aims to understand how genes are activated and deactivated within cells, revealing insights into biological processes and disease mechanisms. Current research heavily utilizes machine learning, employing graph neural networks, transformer models, and various contrastive learning approaches to analyze complex relationships within and between gene expression datasets, often integrating this data with other omics data or spatial information. These advancements improve disease classification, biomarker identification, and drug repurposing, offering significant potential for personalized medicine and a deeper understanding of biological systems.