Data-driven computational frameworks for modelling RNA functional properties

This thesis develops computer-based methods to help scientists better understand RNA, a key molecule in our cells that controls gene activity. The research focuses on three important questions: where RNA goes in the cell, which microRNAs bind to which targets, and which RNAs interact with which proteins. Each method is carefully designed using advanced machine or deep learning techniques to address the unique challenges of its task. Tests on large-scale datasets show that the methods work accurately and reliably. These tools will support future research by providing new ways to explore the roles of RNA in gene regulation.