Predicting biological function from sequence and large-scale experimental data is crucial for understanding regulatory mechanisms in complex systems. However, functional annotation remains a major challenge, particularly in non-model organisms. To address this, we leverage machine learning and large-scale datasets to predict key regulatory features in biological systems. By identifying sequence-based determinants of function, we enable de novo discovery of signaling interactions and gene regulatory elements across species. These predictive models provide scalable frameworks for decoding biological complexity, offering new insights into cellular regulation. Ultimately, our approach contributes to the broader goals of systems biology and the development of data-driven strategies for understanding biological function.
02/04/2025
April 2nd at 12:30 PM
Aula "Franco Tatò" (Via dei Sardi 70, II piano
Prof. Ross Sozzani
NC State University, USA
"Predicted or Not, Here They Come: Shedding Light on Domains and Their Grammar"