RNA-protein interactions play essential roles in many biological processes, such as gene regulation and fundamental cellular processes related to human, animal, and plant diseases. However, the patterns of these interactions are not fully understood. The experimental methods to solve this problem are expensive and time-consuming. Therefore, there is a compelling need for developing reliable computational methods. Predicting these interactions requires structural information about RNA and protein, which is not always available. On the other hand, results of the research on transformers show that they can efficiently extract biochemical, biophysical, and structural features from molecule sequences.
In this seminar, ProtAlbert and DNABERT transformers are used to extract a proper representation of RNA and protein sequences, in order to predict the interactions between these two molecules using a learning model.

RNA-protein interactions play essential roles in many biological processes, such as gene regulation and fundamental cellular processes related to human, animal, and plant diseases. However, the patterns of these interactions are not fully understood. The experimental methods to solve this problem are expensive and time-consuming. Therefore, there is a compelling need for developing reliable computational methods. Predicting these interactions requires structural information about RNA and protein, which is not always available. On the other hand, results of the research on transformers show that they can efficiently extract biochemical, biophysical, and structural features from molecule sequences.
In this seminar, ProtAlbert and DNABERT transformers are used to extract a proper representation of RNA and protein sequences, in order to predict the interactions between these two molecules using a learning model.