Gene Expression Prediction

Gene expression prediction aims to accurately forecast the activity levels of genes based on various input data, primarily genomic sequences or histology images. Current research heavily utilizes deep learning, employing transformer-based architectures and other neural networks to model complex relationships between input features and gene expression levels, often incorporating multi-scale or multi-modal information. This field is crucial for advancing our understanding of biological processes and diseases, with applications ranging from improved diagnostics and personalized medicine to drug discovery and development. The development of more accurate and efficient prediction models is driving progress in these areas.