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

由监督深度学习模型增强的连接酶核酶定向进化的结果已发表在高影响力的同行评审期刊上，并在一次国际会议上发表。作为后续工作，我探索了使用无监督模型来产生新的功能自切割核酶。我在Twister自裂核酶家族序列上训练了三个生成模型。这些计算工作是我在哈佛医学院访问期间进行的。对这些模型的实验评估显示了有希望的初步结果。总的来说，这些工作表明，深度学习模型可以用于设计功能核酶序列在监督和无监督的方式。

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.