Abstract:
This report outlines the methodology, execution, and results of predicting the three- dimensional (3D) structures of miRNA sequences using AI and machine learning tech- niques. Using the RNAfold tool to predict the secondary structure of miRNA sequences, we were able to extract features that were used for predicting 3D structures using the SimRNA tool. These predictions were then utilized as labels for training machine learn- ing models aimed at further understanding miRNA’s functionality and interactions. This report provides a comprehensive overview of the steps, challenges, and future directions in RNA structure prediction and its applications in bioinformatics.
