Machine learning has the ability to rationalize fundamental principles from complex data. Most recently, the application of deep learning approaches has seen spectacular successes due to their -resilience against over-training and noise in data sets. This project will train and apply variational autoencoders to i) predict compatibility between protein sequence and structure, and ii) rationalize and predict function of aminoacyl-tRNA synthetases from library sequence data.

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