Optimization of ribozyme activity using large-scale nucleic acid sequence data analysis by machine learning.

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The results from the directed evolution of ligase ribozymes augmented by supervised deep learning model have been published in a high impact peer reviewed journal and presented on an international conference. As a follow up work, I explored the use of unsupervised models to generate novel functional self-cleaving ribozymes. I trained three generative models on sequences of Twister self-cleaving ribozyme family. These computational works were conducted during my visiting position in Harvard Medical School. Experimental evaluation of these models showed promising preliminary results. Overall these works have shown that deep learning models can be used to design functional ribozymes sequences in both supervised and unsupervised manner.