Gene Representation

Gene representation research focuses on developing effective numerical representations of gene sequences and their associated data to facilitate analysis and prediction in genomics. Current efforts center on deep learning models, including transformer architectures (like DNABert and variations) and graph neural networks, often employing techniques like masked language modeling, contrastive learning, and optimal transport to capture complex relationships within and between genes and other biological data (e.g., images, spatial transcriptomics). These advancements are crucial for improving the accuracy and efficiency of tasks such as gene function prediction, disease diagnosis, and drug discovery, ultimately accelerating biological research and personalized medicine.