| A deep learning approach to antibiotic discovery JM Stokes, K Yang, K Swanson, W Jin, A Cubillos-Ruiz, NM Donghia, ... Cell 180 (4), 688-702. e13, 2020 | 1642 | 2020 |
| Analyzing learned molecular representations for property prediction K Yang, K Swanson, W Jin, C Coley, P Eiden, H Gao, A Guzman-Perez, ... Journal of chemical information and modeling 59 (8), 3370-3388, 2019 | 1252 | 2019 |
| Mammographic breast density assessment using deep learning: clinical implementation CD Lehman, A Yala, T Schuster, B Dontchos, M Bahl, K Swanson, ... Radiology 290 (1), 52-58, 2019 | 248 | 2019 |
| Uncertainty quantification using neural networks for molecular property prediction L Hirschfeld, K Swanson, K Yang, R Barzilay, CW Coley Journal of Chemical Information and Modeling 60 (8), 3770-3780, 2020 | 186 | 2020 |
| Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii G Liu, DB Catacutan, K Rathod, K Swanson, W Jin, JC Mohammed, ... Nature Chemical Biology 19 (11), 1342-1350, 2023 | 88 | 2023 |
| From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment K Swanson, E Wu, A Zhang, AA Alizadeh, J Zou Cell, 2023 | 85 | 2023 |
| Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens Z Wu, AE Trevino, E Wu, K Swanson, HJ Kim, HB D’Angio, R Preska, ... Nature Biomedical Engineering 6 (12), 1435-1448, 2022 | 45 | 2022 |
| Deep learning for automated classification and characterization of amorphous materials K Swanson, S Trivedi, J Lequieu, K Swanson, R Kondor Soft matter 16 (2), 435-446, 2020 | 39 | 2020 |
| Rationalizing text matching: Learning sparse alignments via optimal transport K Swanson, L Yu, T Lei arXiv preprint arXiv:2005.13111, 2020 | 37 | 2020 |
| Improving molecular design by stochastic iterative target augmentation K Yang, W Jin, K Swanson, R Barzilay, T Jaakkola International Conference on Machine Learning, 10716-10726, 2020 | 21 | 2020 |
| Message passing neural networks for molecular property prediction K Swanson Massachusetts Institute of Technology, 2019 | 21 | 2019 |
| 7-UP: Generating in silico CODEX from a small set of immunofluorescence markers E Wu, AE Trevino, Z Wu, K Swanson, HJ Kim, HB D’Angio, R Preska, ... PNAS nexus 2 (6), pgad171, 2023 | 12 | 2023 |
| Building a Production Model for Retrieval-Based Chatbots K Swanson, L Yu, C Fox, J Wohlwend, T Lei 1st Workshop on NLP for Conversational AI at the 57th Annual Meeting of the …, 2019 | 11 | 2019 |
| SPACE-GM: geometric deep learning of disease-associated microenvironments from multiplex spatial protein profiles Z Wu, AE Trevino, E Wu, K Swanson, HJ Kim, HB D’Angio, R Preska, ... bioRxiv, 2022.05. 12.491707, 2022 | 7 | 2022 |
| Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN Y Rosen, M Brbić, Y Roohani, K Swanson, Z Li, J Leskovec Nature Methods, 1-9, 2024 | 5 | 2024 |
| File matching with rationale alignment using neural networks and an optimal transport algorithm K Swanson, L Yu, T Lei US Patent 11,238,278, 2022 | 3 | 2022 |
| Predicting Immune Escape with Pretrained Protein Language Model Embeddings K Swanson, H Chang, J Zou Machine Learning in Computational Biology, 110-130, 2022 | 2 | 2022 |
| Generative AI for designing and validating easily synthesizable and structurally novel antibiotics K Swanson, G Liu, DB Catacutan, A Arnold, J Zou, JM Stokes Nature Machine Intelligence 6 (3), 338-353, 2024 | 1 | 2024 |
| ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries K Swanson, P Walther, J Leitz, S Mukherjee, JC Wu, RV Shivnaraine, ... bioRxiv, 2023.12. 28.573531, 2023 | 1 | 2023 |
| Monte Carlo Tree Search for Interpreting Stress in Natural Language K Swanson, J Hsu, M Suzgun arXiv preprint arXiv:2204.08105, 2022 | | 2022 |
Tommi JaakkolaMITcsail.mit.edu의 이메일 확인됨
