| Highly accurate protein structure prediction with AlphaFold J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... nature 596 (7873), 583-589, 2021 | 32930 | 2021 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 3386 | 2023 |
| Accurate structure prediction of biomolecular interactions with AlphaFold 3 J Abramson, J Adler, J Dunger, R Evans, T Green, A Pritzel, ... Nature 630 (8016), 493-500, 2024 | 3161 | 2024 |
| Highly accurate protein structure prediction for the human proteome K Tunyasuvunakool, J Adler, Z Wu, T Green, M Zielinski, A Žídek, ... Nature 596 (7873), 590-596, 2021 | 2596 | 2021 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 1283 | 2024 |
| Learned primal-dual reconstruction J Adler, O Öktem IEEE transactions on medical imaging, 2018 | 974 | 2018 |
| Solving ill-posed inverse problems using iterative deep neural networks J Adler, O Öktem Inverse Problems 33 (12), 124007, 2017 | 769 | 2017 |
| Banach Wasserstein GAN J Adler, S Lunz Neural Information Processing Systems, 2018 | 377 | 2018 |
| Model-based learning for accelerated, limited-view 3-D photoacoustic tomography A Hauptmann, F Lucka, M Betcke, N Huynh, J Adler, B Cox, P Beard, ... IEEE transactions on medical imaging 37 (6), 1382-1393, 2018 | 372 | 2018 |
| Applying and improving AlphaFold at CASP14 J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... Proteins: Structure, Function, and Bioinformatics 89 (12), 1711-1721, 2021 | 369 | 2021 |
| High Accuracy Protein Structure Prediction Using Deep Learning J Jumper, R Evans, A Pritzel, T Green, M Figurnov, K Tunyasuvunakool, ... Fourteenth Critical Assessment of Techniques for Protein Structure Prediction, 2020 | 166 | 2020 |
| Deep Bayesian Inversion J Adler, O Öktem arXiv preprint arXiv:1811.05910, 2018 | 158 | 2018 |
| Operator Discretization Library (ODL) J Adler, H Kohr, O Öktem Software available from https://github. com/odlgroup/odl, 2017 | 152* | 2017 |
| Computational predictions of protein structures associated with COVID-19 J Jumper, K Tunyasuvunakool, P Kohli, D Hassabis, the AlphaFold Team DeepMind website, 2020 | 123* | 2020 |
| Continuous diffusion for categorical data S Dieleman, L Sartran, A Roshannai, N Savinov, Y Ganin, PH Richemond, ... arXiv preprint arXiv:2211.15089, 2022 | 97 | 2022 |
| Multi-scale learned iterative reconstruction A Hauptmann, J Adler, S Arridge, O Öktem IEEE transactions on computational imaging 6, 843-856, 2020 | 56 | 2020 |
| Task adapted reconstruction for inverse problems J Adler, S Lunz, O Verdier, CB Schönlieb, O Öktem Inverse Problems 38 (7), 075006, 2022 | 54 | 2022 |
| Inferring a continuous distribution of atom coordinates from cryo-EM images using VAEs D Rosenbaum, M Garnelo, M Zielinski, C Beattie, E Clancy, A Huber, ... arXiv preprint arXiv:2106.14108, 2021 | 49 | 2021 |
| Learning to solve inverse problems using Wasserstein loss J Adler, A Ringh, O Öktem, J Karlsson NIPS 2017 Optimal Transport and Machine Learning, 2017 | 42 | 2017 |
| Computational models in the service of X‐ray and cryo‐electron microscopy structure determination A Kryshtafovych, J Moult, R Albrecht, GA Chang, K Chao, A Fraser, ... Proteins: Structure, Function, and Bioinformatics 89 (12), 1633-1646, 2021 | 38 | 2021 |
Ozan ÖktemDepartment of Mathematics, KTH - Royal Institute of Technology在 oktem.se 的电子邮件经过验证