| Generalized biomolecular modeling and design with RoseTTAFold All-Atom R Krishna, J Wang, W Ahern, P Sturmfels, P Venkatesh, I Kalvet, GR Lee, ... Science 384 (6693), eadl2528, 2024 | 380 | 2024 |
| Improving performance of deep learning models with axiomatic attribution priors and expected gradients G Erion, JD Janizek, P Sturmfels, SM Lundberg, SI Lee Nature machine intelligence 3 (7), 620-631, 2021 | 347* | 2021 |
| Visualizing the impact of feature attribution baselines P Sturmfels, S Lundberg, SI Lee Distill 5 (1), e22, 2020 | 301* | 2020 |
| Explaining explanations: Axiomatic feature interactions for deep networks JD Janizek, P Sturmfels, SI Lee Journal of Machine Learning Research 22 (104), 1-54, 2021 | 177 | 2021 |
| Automated brain masking of fetal functional MRI with open data S Rutherford, P Sturmfels, M Angstadt, J Hect, J Wiens, ... Neuroinformatics 20 (1), 173-185, 2022 | 48* | 2022 |
| A domain guided CNN architecture for predicting age from structural brain images P Sturmfels, S Rutherford, M Angstadt, M Peterson, C Sripada, J Wiens Machine learning for healthcare conference, 295-311, 2018 | 32 | 2018 |
| FoggySight: a scheme for facial lookup privacy I Evtimov, P Sturmfels, T Kohno Proceedings on Privacy Enhancing Technologies 3, 204-226, 2021 | 30 | 2021 |
| Profile prediction: An alignment-based pre-training task for protein sequence models P Sturmfels, J Vig, A Madani, NF Rajani arXiv preprint arXiv:2012.00195, 2020 | 23 | 2020 |
| Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer’s disease neuropathologies N Beebe-Wang, S Celik, E Weinberger, P Sturmfels, PL De Jager, ... Nature Communications 12 (1), 5369, 2021 | 22 | 2021 |
| Select and permute: An improved online framework for scheduling to minimize weighted completion time S Khuller, J Li, P Sturmfels, K Sun, P Venkat Theoretical Computer Science 795, 420-431, 2019 | 20 | 2019 |
| The Lair: a resource for exploratory analysis of published RNA-Seq data H Pimentel, P Sturmfels, N Bray, P Melsted, L Pachter BMC bioinformatics 17, 1-6, 2016 | 20 | 2016 |
| Seq2MSA: A Language Model for Protein Sequence Diversification P Sturmfels, R Rao, R Verkuil, Z Lin, O Kabeli, T Sercu, A Lerer, A Rives MLSB Workshop (NeurIPS), 2022 | 2 | 2022 |
| Explainable Machine Learning and Applications in Protein-Ligand Complex Structure Prediction P Sturmfels, D Baker, S Wang, F Dimaio | | 2024 |
| Systems and methods for alignment-based pre-training of protein prediction models P Sturmfels, A Madani, J Vig, N Rajani US Patent App. 17/153,164, 2022 | | 2022 |
Su-In LeeComputer Science & Engineering, University of Washingtoncs.washington.edu의 이메일 확인됨