Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).

Article  CAS  PubMed  Google Scholar 

Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).

Article  CAS  PubMed  Google Scholar 

Coutant, A. et al. Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast. Proc. Natl Acad. Sci. USA 116, 18142–18147 (2019).

Article  CAS  PubMed  PubMed Central  Google Scholar 

King, R. D. et al. The automation of science. Science 324, 85–89 (2009).

Article  CAS  PubMed  Google Scholar 

Schrittwieser, J. et al. Mastering Atari, Go, chess and shogi by planning with a learned model. Nature 588, 604–609 (2020).

Article  CAS  PubMed  Google Scholar 

Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).

Article  CAS  PubMed  Google Scholar 

Silver, D. et al. A general reinforcement learning algorithm that masters chess, dhogi, and Go through self-play. Science 362, 1140–1144 (2018).

Article  CAS  PubMed  Google Scholar 

Silver, D., Singh, S., Precup, D. & Sutton, R. S. Reward is enough. Artif. Intell. 299, 103535 (2021).

Article  Google Scholar 

Tesauro, G. TD-Gammon, a self-teaching backgammon program, achieves master-level play. Neural Comput. 6, 215–219 (1994).

Article  Google Scholar 

Tesauro, G. & Galperin, G. On-line policy improvement using Monte-Carlo Search. In Advances in Neural Information Processing Systems (eds Mozer, M. C. et al) (MIT Press, 1996); https://proceedings.neurips.cc/paper_files/paper/1996/file/996009f2374006606f4c0b0fda878af1-Paper.pdf

Fel’dbaum, A. A. Theory of dual control. Autom. Remote Control 21, 1240–1249 (1960).

Google Scholar 

Witten, I. H. The apparent conflict between estimation and control—a survey of the two-armed bandit problem. J. Frankl. Inst. 301, 161–189 (1976).

Article  Google Scholar 

Patel, S. & Gupta, R. S. Robust demarcation of fourteen different species groups within the genus Streptococcus based on genome-based phylogenies and molecular signatures. Infect. Genet. Evol. 66, 130–151 (2018).

Article  CAS  PubMed  Google Scholar 

van de Rijn, I. & Kessler, R. E. Growth characteristics of group A streptococci in a new chemically defined medium. Infect. Immun. 27, 444–448 (1980).

Article  PubMed  PubMed Central  Google Scholar 

Lewin, G. R., Stocke, K. S., Lamont, R. J. & Whiteley, M. A quantitative framework reveals traditional laboratory growth is a highly accurate model of human oral infection. Proc. Natl Acad. Sci USA. https://doi.org/10.1073/pnas.2116637119 (2022).

King, R. D. et al. Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 247–252 (2004).

Article  CAS  PubMed  Google Scholar 

Magnúsdóttir, S. et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat Biotechnol. https://doi.org/10.1038/nbt.3703 (2017).

Jijakli, K. & Jensen, P. A. Metabolic modeling of Streptococcus mutans reveals complex nutrient requirements of an oral pathogen. mSystems. https://doi.org/10.1128/mSystems.00529-19 (2019).

Dama, A. C. & Jensen, P. A. PlatePlan. GitHub https://github.com/jensenlab/PlatePlan (2020).

Bellman, R. A Markovian decision process. J. Math. Mech. 6, 679–684 (1957).

Google Scholar 

Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).

Article  Google Scholar 

Glorot, X., Bordes, A. & Bengio, Y. Deep sparse rectifier neural networks. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics Vol. 15 (eds Gordon, G. et al.) 315–323 (PMLR, 2011); https://proceedings.mlr.press/v15/glorot11a.html

Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. In 3rd International Conference on Learning Representations (eds Bengio, Y. & LeCun, Y.) Preprint at arXiv https://doi.org/10.48550/arXiv.1412.6980 (2015).

Akiba, T., Sano, S., Yanase, T., Ohta, T. & Koyama, M. Optuna: a next-generation hyperparameter optimization framework. In Proceedings of the 25rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining https://doi.org/10.1145/3292500.3330701 (Association for Computing Machinery, 2019).

Holland, J. H. Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence (MIT Press, 1992).

Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32 (eds Wallach, H. et al.) 8024–8035 (Curran Associates, Inc., 2019); http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf