Sequence Relationship

Sequence relationship analysis focuses on understanding how the order of elements within a sequence (e.g., DNA, protein, user behavior) influences its overall properties and function. Current research emphasizes developing efficient and interpretable models, including graph neural networks and recurrent neural networks, to capture complex relationships within and between sequences, often incorporating techniques like contrastive learning and hierarchical representations to improve performance and scalability. These advancements are crucial for diverse applications, such as genomic interpretation, fraud detection, and protein structure prediction, by enabling more accurate predictions and a deeper understanding of underlying biological mechanisms.