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Cryo-electron microscopy (cryo-EM) has revolutionized experimental protein structure determination.
The" wake-sleep" algorithm for unsupervised neural networks
G. E. Hinton, P. Dayan, B. J. Frey, and R. M. Neal · 1995
Earlier work this paper cites.
Optimal transport: old and new
C. Villani · 2009
Earlier work this paper cites.
Simulation of transmission electron microscope images of biological specimens
H. Rullgård, L.-G. Öfverstedt, S. Masich, B. Daneholt, and O. Öktem · 2011
Earlier work this paper cites.
Relion: implementation of a bayesian approach to cryo-em structure determination
S. H. Scheres · 2012
Earlier work this paper cites.
Likelihood-based classification of cryo-em images using frealign
D. Lyumkis, A. F. Brilot, D. L. Theobald, and N. Grigorieff · 2013
Earlier work this paper cites.
Image formation modeling in cryo-electron microscopy
M. Vulović, R. B. Ravelli, L. J. van Vliet, A. J. Koster, I. Lazić, U. Lücken, H. Rullgård, O. Öktem, and B. Rieger · 2013
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Earlier work this paper cites.
J. L. Ba, J. R. Kiros, and G. E. Hinton · 2016
Earlier work this paper cites.
Importance weighted autoencoders
Y. Burda, R. Grosse, and R. Salakhutdinov · 2016
Earlier work this paper cites.
Elbo surgery: yet another way to carve up the variational evidence lower bound
M. D. Hoffman and M. J. Johnson · 2016
Earlier work this paper cites.
A water-mediated allosteric network governs activation of aurora kinase a
S. Cyphers, E. F. Ruff, J. M. Behr, J. D. Chodera, and N. M. Levinson · 2017
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner · 2017
Cited alongside, same era.
cryosparc: algorithms for rapid unsupervised cryo-em structure determination
A. Punjani, J. L. Rubinstein, D. J. Fleet, and M. A. Brubaker · 2017
Cited alongside, same era.
Learned primal-dual reconstruction
J. Adler and O. Öktem · 2018
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs, 2018
J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang · 2018
Cited alongside, same era.
Characterisation of molecular motions in cryo-em single-particle data by multi-body refinement in relion
Sphire-cryolo is a fast and accurate fully automated particle picker for cryo-em
T. Wagner, F. Merino, M. Stabrin, T. Moriya, C. Antoni, A. Apelbaum, P. Hagel, O. Sitsel, T. Raisch, D. Prumbaum, et al · 2019
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The DeepMind JAX Ecosystem, 2020
I. Babuschkin, K. Baumli, A. Bell, S. Bhupatiraju, J. Bruce, P. Buchlovsky, D. Budden, T. Cai, A. Clark, I. Danihelka, C. Fantacci, J. Godwin, C. Jones, T. Hennigan, M. Hessel, S. Kapturowski, T. Keck, I. Kemaev, M. King, L. Martens, V. Mikulik, T. Norman, J. Quan, G. Papamakarios, R. Ring, F. Ruiz, A. Sanchez, R. Schneider, E. Sezener, S. Spencer, S. Srinivasan, W. Stokowiec, and F. Viola · 2020
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Topaz-denoise: general deep denoising models for cryoem and cryoet
T. Bepler, K. Kelley, A. J. Noble, and B. Berger · 2020
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Haiku: Sonnet for JAX, 2020
T. Hennigan, T. Cai, T. Norman, and I. Babuschkin · 2020
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Optax: composable gradient transformation and optimisation, in jax!, 2020
M. Hessel, D. Budden, F. Viola, M. Rosca, E. Sezener, and T. Hennigan · 2020
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T. Nakane, D. Kimanius, E. Lindahl, and S. H. Scheres · 2018
Cited alongside, same era.
A dynamic mechanism for allosteric activation of aurora kinase a by activation loop phosphorylation
E. F. Ruff, J. M. Muretta, A. R. Thompson, E. W. Lake, S. Cyphers, S. K. Albanese, S. M. Hanson, J. M. Behr, D. D. Thomas, J. D. Chodera, et al · 2018
Cited alongside, same era.
Fret as a biomolecular research tool—understanding its potential while avoiding pitfalls
W. R. Algar, N. Hildebrandt, S. S. Vogel, and I. L. Medintz · 2019
Cited alongside, same era.
Solving inverse problems using data-driven models
S. Arridge, P. Maass, O. Öktem, and C.-B. Schönlieb · 2019
Cited alongside, same era.
An introduction to variational autoencoders
D. P. Kingma and M. Welling · 2019
Cited alongside, same era.
Protein structure prediction using multiple deep neural networks in the 13th critical assessment of protein structure prediction (casp13)
A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. Qin, A. Žídek, A. W. R. Nelson, A. Bridgland, H. Penedones, S. Petersen, K. Simonyan, S. Crossan, P. Kohli, D. T. Jones, D. Silver, K. Kavukcuoglu, and D. Hassabis · 2019
Cited alongside, same era.
High accuracy protein structure prediction using deep learning
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, K. Tunyasuvunakool, O. Ronneberger, R. Bates, A. Žídek, A. Bridgland, C. Meyer, S. A. A. Kohl, A. Potapenko, A. J. Ballard, A. Cowie, B. Romera-Paredes, S. Nikolov, R. Jain, J. Adler, T. Back, S. Petersen, D. Reiman, M. Steinegger, M. Pacholska, D. Silver, O. Vinyals, A. W. Senior, K. Kavukcuoglu, P. Kohli, and D. Hassabis · 2020
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Tutorial on variational autoencoders
C. Doersch · 2021
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Exploiting prior knowledge about biological macromolecules in cryo-em structure determination
D. Kimanius, G. Zickert, T. Nakane, J. Adler, S. Lunz, C.-B. Schönlieb, O. Öktem, and S. H. Scheres · 2021
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3d variability analysis: Resolving continuous flexibility and discrete heterogeneity from single particle cryo-em
A. Punjani and D. J. Fleet · 2021
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Cryodrgn: reconstruction of heterogeneous cryo-em structures using neural networks
E. D. Zhong, T. Bepler, B. Berger, and J. H. Davis · 2021
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