Biological Process

Research on biological processes is increasingly focused on developing robust methods for analyzing complex, heterogeneous data to identify shared patterns across individuals and species. Current approaches leverage techniques like optimal transport for data integration, Petri nets for modeling dynamic systems (e.g., glucose regulation), and machine learning algorithms (including deep learning and convolutional neural networks) for prediction and analysis, often incorporating novel data representations like k-mers and chaos game representations. These advancements aim to improve our understanding of fundamental biological mechanisms, enabling more accurate predictions of interactions (e.g., protein-protein interactions) and facilitating comparisons of anatomical variations across time series data. Ultimately, this work contributes to a deeper understanding of biological processes and informs applications in diverse fields, including medicine and agriculture.