Motif Discovery

Motif discovery focuses on identifying recurring patterns (motifs) within various data types, including biological sequences, time series, and graphs, aiming to uncover underlying structures and relationships. Current research emphasizes developing data-driven methods, often incorporating deep learning and graph neural networks, to extract motifs efficiently and accurately, with a growing focus on improving model interpretability and explainability. These advancements have significant implications across diverse fields, enabling improved understanding of biological processes, enhanced time series analysis, and more effective graph-based machine learning applications.