Gene Expression

Gene expression analysis focuses on understanding how genes are activated and produce proteins, providing insights into cellular processes and disease mechanisms. Current research heavily utilizes machine learning, particularly deep learning models like transformers, graph neural networks, and variational autoencoders, to analyze high-dimensional gene expression data, often integrating it with spatial transcriptomics and histology images. These advancements enable improved disease prediction, biomarker discovery, and personalized medicine strategies, particularly in cancer research, by revealing complex relationships between gene expression patterns and clinical outcomes. The integration of large language models further enhances data interpretation and facilitates more efficient exploration of gene expression datasets.