Expression Profile

Expression profiling aims to characterize the activity of genes within a biological sample, often using techniques like microarrays or spatial transcriptomics. Current research heavily utilizes deep learning models, including transformers, autoencoders, and graph neural networks, to predict gene expression from readily available histological images, addressing limitations of traditional methods in cost, resolution, and data scarcity. This work is significantly impacting disease diagnosis and treatment by enabling more accurate and efficient molecular phenotyping, particularly in cancer research, where it facilitates improved subtyping, prognosis, and drug repurposing.