Model-based reinforcement learning for biological sequence design
暂无分享,去创建一个
David Dohan | David Belanger | Lucy J. Colwell | Ramya Deshpande | Kevin Murphy | Christof Angermueller | David Dohan | David Belanger | Christof Angermueller | Ramya Deshpande | Kevin Murphy
[1] Zachary Wu,et al. Machine learning-assisted directed protein evolution with combinatorial libraries , 2019, Proceedings of the National Academy of Sciences.
[2] Regina Barzilay,et al. Learning Multimodal Graph-to-Graph Translation for Molecular Optimization , 2018, ICLR.
[3] Brendan J. Frey,et al. Generating and designing DNA with deep generative models , 2017, ArXiv.
[4] Georg Seelig,et al. Human 5′ UTR design and variant effect prediction from a massively parallel translation assay , 2018, Nature Biotechnology.
[5] Samy Bengio,et al. Neural Combinatorial Optimization with Reinforcement Learning , 2016, ICLR.
[6] Li Li,et al. Optimization of Molecules via Deep Reinforcement Learning , 2018, Scientific Reports.
[7] Mohamed Ahmed,et al. Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design , 2018, ICLR.
[8] Xiaowo Wang,et al. Synthetic Promoter Design in Escherichia coli based on Generative Adversarial Network , 2019 .
[9] Thomas A. Hopf,et al. Protein 3D Structure Computed from Evolutionary Sequence Variation , 2011, PloS one.
[10] Frances H. Arnold,et al. Design by Directed Evolution. , 1998 .
[11] Alán Aspuru-Guzik,et al. Inverse molecular design using machine learning: Generative models for matter engineering , 2018, Science.
[12] Kam-Fai Wong,et al. Integrating planning for task-completion dialogue policy learning , 2018, ACL.
[13] Tom Schaul,et al. Natural Evolution Strategies , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).
[14] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[15] Tom Schaul,et al. Unifying Count-Based Exploration and Intrinsic Motivation , 2016, NIPS.
[16] Ziheng Wang,et al. Antibody complementarity determining region design using high-capacity machine learning , 2019, bioRxiv.
[17] Dick de Ridder,et al. Designing Eukaryotic Gene Expression Regulation Using Machine Learning. , 2020, Trends in biotechnology.
[18] Jacob Witten,et al. Deep learning regression model for antimicrobial peptide design , 2019, bioRxiv.
[19] E. Shakhnovich,et al. A new approach to the design of stable proteins. , 1993, Protein engineering.
[20] Jennifer Listgarten,et al. Design by adaptive sampling , 2018, ArXiv.
[21] Jasper Snoek,et al. Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.
[22] T. N. Bhat,et al. The Protein Data Bank , 2000, Nucleic Acids Res..
[23] Joelle Pineau,et al. An Actor-Critic Algorithm for Sequence Prediction , 2016, ICLR.
[24] Frank Hutter,et al. Learning to Design RNA , 2018, ICLR.
[25] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[26] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[27] Yoshua Bengio,et al. Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon , 2018, Eur. J. Oper. Res..
[28] James Zou,et al. Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions , 2018, ArXiv.
[29] R. Jernigan,et al. Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. , 1996, Journal of molecular biology.
[30] Le Song,et al. 2 Common Formulation for Greedy Algorithms on Graphs , 2018 .
[31] Alok Aggarwal,et al. Regularized Evolution for Image Classifier Architecture Search , 2018, AAAI.
[32] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.
[33] Dale Schuurmans,et al. Reward Augmented Maximum Likelihood for Neural Structured Prediction , 2016, NIPS.
[34] Richard S. Sutton,et al. Dyna, an integrated architecture for learning, planning, and reacting , 1990, SGAR.
[35] Pieter Abbeel,et al. Model-Ensemble Trust-Region Policy Optimization , 2018, ICLR.
[36] Shie Mannor,et al. A Tutorial on the Cross-Entropy Method , 2005, Ann. Oper. Res..
[37] Kevin Murphy,et al. A view of estimation of distribution algorithms through the lens of expectation-maximization , 2019, GECCO Companion.
[38] Xi Chen,et al. Evolution Strategies as a Scalable Alternative to Reinforcement Learning , 2017, ArXiv.
[39] Alec Radford,et al. Proximal Policy Optimization Algorithms , 2017, ArXiv.
[40] Regina Barzilay,et al. Generative Models for Graph-Based Protein Design , 2019, DGS@ICLR.
[41] Sari Sabban,et al. RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative neural network , 2019, bioRxiv.
[42] Arpit Joshi,et al. Generating protein sequences from antibiotic resistance genes data using Generative Adversarial Networks , 2019, ArXiv.
[43] R. J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[44] John C. Duchi,et al. Derivative Free Optimization Via Repeated Classification , 2018, AISTATS.
[45] David Barber,et al. Optimization by Variational Bounding , 2013, ESANN.
[46] Alán Aspuru-Guzik,et al. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.
[47] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[48] Max Welling,et al. Attention, Learn to Solve Routing Problems! , 2018, ICLR.
[49] Sergey Levine,et al. Model-Based Reinforcement Learning for Atari , 2019, ICLR.
[50] Sergey Levine,et al. When to Trust Your Model: Model-Based Policy Optimization , 2019, NeurIPS.
[51] Carl E. Rasmussen,et al. PILCO: A Model-Based and Data-Efficient Approach to Policy Search , 2011, ICML.
[52] Jos'e Miguel Hern'andez-Lobato,et al. Constrained Bayesian Optimization for Automatic Chemical Design , 2017 .
[53] Marc G. Bellemare,et al. A Distributional Perspective on Reinforcement Learning , 2017, ICML.
[54] Jennifer Listgarten,et al. Conditioning by adaptive sampling for robust design , 2019, ICML.
[55] T. Hwa,et al. Identification of direct residue contacts in protein–protein interaction by message passing , 2009, Proceedings of the National Academy of Sciences.
[56] Kirthevasan Kandasamy,et al. ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations , 2019, AISTATS.
[57] Matt J. Kusner,et al. Grammar Variational Autoencoder , 2017, ICML.
[58] Nando de Freitas,et al. Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.