Navigating the protein fitness landscape with Gaussian processes
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[1] M. L. Fisher,et al. An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..
[2] Michel Minoux,et al. Accelerated greedy algorithms for maximizing submodular set functions , 1978 .
[3] Alexander K. Kelmans,et al. Multiplicative submodularity of a matrix's principal minor as a function of the set of its rows and some combinatorial applications , 1983, Discret. Math..
[4] H. Barnes,et al. Expression and enzymatic activity of recombinant cytochrome P450 17 alpha-hydroxylase in Escherichia coli. , 1991, Proceedings of the National Academy of Sciences of the United States of America.
[5] I. Guttman,et al. Comparing probabilistic methods for outlier detection in linear models , 1993 .
[6] S. Gavrilets. Evolution and speciation on holey adaptive landscapes. , 1997, Trends in ecology & evolution.
[7] S. L. Mayo,et al. De novo protein design: fully automated sequence selection. , 1997, Science.
[8] W. Mandecki. The game of chess and searches in protein sequence space , 1998 .
[9] Donald R. Jones,et al. Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..
[10] David Barber,et al. Bayesian Classification With Gaussian Processes , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[11] Roger Woodard,et al. Interpolation of Spatial Data: Some Theory for Kriging , 1999, Technometrics.
[12] Anthony D. Keefe,et al. Functional proteins from a random-sequence library , 2001, Nature.
[13] Jon Lee. Maximum entropy sampling , 2001 .
[14] Peter Auer,et al. Using Confidence Bounds for Exploitation-Exploration Trade-offs , 2003, J. Mach. Learn. Res..
[15] D. M. Taverna,et al. Why are proteins marginally stable? , 2002, Proteins.
[16] Niles A Pierce,et al. Protein design is NP-hard. , 2002, Protein engineering.
[17] C. Otey,et al. High-throughput screen for aromatic hydroxylation. , 2003, Methods in molecular biology.
[18] F. Arnold,et al. Thermostabilization of a Cytochrome P450 Peroxygenase , 2003, Chembiochem : a European journal of chemical biology.
[19] C. Otey. High-throughput carbon monoxide binding assay for cytochromes p450. , 2003, Methods in molecular biology.
[20] D. Axe. Estimating the prevalence of protein sequences adopting functional enzyme folds. , 2004, Journal of molecular biology.
[21] Hongyi Zhou,et al. An accurate, residue‐level, pair potential of mean force for folding and binding based on the distance‐scaled, ideal‐gas reference state , 2004, Protein science : a publication of the Protein Society.
[22] Christopher A. Voigt,et al. Functional evolution and structural conservation in chimeric cytochromes p450: calibrating a structure-guided approach. , 2004, Chemistry & biology.
[23] Andreas Krause,et al. Near-optimal sensor placements in Gaussian processes , 2005, ICML.
[24] C. Wilke,et al. On the conservative nature of intragenic recombination. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[25] H. A. Orr,et al. The distribution of fitness effects among beneficial mutations in Fisher's geometric model of adaptation. , 2006, Journal of theoretical biology.
[26] Jeffrey B. Endelman,et al. Structure-Guided Recombination Creates an Artificial Family of Cytochromes P450 , 2006, PLoS biology.
[27] Andreas Krause,et al. Near-optimal Observation Selection using Submodular Functions , 2007, AAAI.
[28] F. Arnold,et al. Diversification of catalytic function in a synthetic family of chimeric cytochrome p450s. , 2007, Chemistry & biology.
[29] Manfred K. Warmuth,et al. Engineering proteinase K using machine learning and synthetic genes , 2007, BMC biotechnology.
[30] F. Arnold,et al. A diverse family of thermostable cytochrome P450s created by recombination of stabilizing fragments , 2007, Nature Biotechnology.
[31] Tao Wang,et al. Automatic Gait Optimization with Gaussian Process Regression , 2007, IJCAI.
[32] John C Whitman,et al. Improving catalytic function by ProSAR-driven enzyme evolution , 2007, Nature Biotechnology.
[33] Eric A. Althoff,et al. De Novo Computational Design of Retro-Aldol Enzymes , 2008, Science.
[34] Warren B. Powell,et al. A Knowledge-Gradient Policy for Sequential Information Collection , 2008, SIAM J. Control. Optim..
[35] Andreas Krause,et al. Toward Community Sensing , 2008, 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008).
[36] Philip A. Romero,et al. Exploring protein fitness landscapes by directed evolution , 2009, Nature Reviews Molecular Cell Biology.
[37] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[38] Andreas Krause,et al. Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting , 2009, IEEE Transactions on Information Theory.
[39] David Baker,et al. An exciting but challenging road ahead for computational enzyme design , 2010, Protein science : a publication of the Protein Society.
[40] Mikhail G. Shapiro,et al. Directed evolution of a magnetic resonance imaging contrast agent for noninvasive imaging of dopamine , 2010, Nature Biotechnology.
[41] F. Arnold,et al. Structure-guided directed evolution of highly selective p450-based magnetic resonance imaging sensors for dopamine and serotonin. , 2012, Journal of molecular biology.
[42] Andreas Krause,et al. Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization , 2012, ICML.
[43] S. Kakade,et al. Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting , 2012, IEEE Transactions on Information Theory.