Advancing RNA-based machine learning models for predicting RNA-RNA interactions
The broader goals of the project include facilitating the exploration of the RNA interactome for researchers and suggesting potential therapeutic interventions for diseases where RNA dysfunction is a significant factor, such as in cancers and genetic disorders.
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RNA-RNA interactions are fundamental to a variety of biological processes, including the regulation of gene expression, the formation of RNA structures, and the interactions between different RNA molecules. Understanding these interactions is crucial for advancements in therapeutic development and biomolecular research. This project aims to build advanced computational models that are RNA-centric, with the goal of enhancing the accuracy of predictions for RNA-RNA interactions. By harnessing extensive high-throughput sequencing data and integrating RNA sequence and structural information, these models are designed to decode complex binding sites and unravel interaction patterns among various RNA molecules. The broader goals of the project include facilitating the exploration of the RNA interactome for researchers and suggesting potential therapeutic interventions for diseases where RNA dysfunction is a significant factor, such as in cancers and genetic disorders.