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Proteins are responsible for the most diverse set of functions in biology.
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M. Ekeberg, C. Lövkvist, Y. Lan, M. Weigt, and E. Aurell, “Improved contact prediction in proteins: using pseudolikelihoods to infer potts models,” Physical Review E
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D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980
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P. A. Romero, T. M. Tran, and A. R. Abate, “Dissecting enzyme function with microfluidic-based deep mutational scanning,” Proceedings of the National Academy of Sciences
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M. A. Stiffler, D. R. Hekstra, and R. Ranganathan, “Evolvability as a function of purifying selection in tem-1 β \beta -lactamase,” Cell
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L. Rockah-Shmuel, Á. Tóth-Petróczy, and D. S. Tawfik, “Systematic mapping of protein mutational space by prolonged drift reveals the deleterious effects of seemingly neutral mutations,” PLoS computational biology
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F. Chollet et al
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2016
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T. A. Hopf, J. B. Ingraham, F. J. Poelwijk, C. P. Schärfe, M. Springer, C. Sander, and D. S. Marks, “Mutation effects predicted from sequence co-variation,” Nature biotechnology
2017
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J. Ingraham and D. Marks, “Variational inference for sparse and undirected models,” in International Conference on Machine Learning
2017
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2015
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2017
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