| Accurate classification of BRCA1 variants with saturation genome editing GM Findlay, RM Daza, B Martin, MD Zhang, AP Leith, M Gasperini, ... Nature 562 (7726), 217-222, 2018 | 795 | 2018 |
| AI for radiographic COVID-19 detection selects shortcuts over signal AJ DeGrave, JD Janizek, SI Lee Nature Machine Intelligence 3 (7), 610-619, 2021 | 632 | 2021 |
| Improving performance of deep learning models with axiomatic attribution priors and expected gradients G Erion, JD Janizek, P Sturmfels, S Lundberg, SI Lee Nature Machine Intelligence, 2019 | 343* | 2019 |
| True to the Model or True to the Data? H Chen, JD Janizek, S Lundberg, SI Lee ICML Workshop on Human Interpretability in Machine Learning, 2020 | 187 | 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 | 175 | 2021 |
| Adversarial deconfounding autoencoder for learning robust gene expression embeddings AB Dincer, JD Janizek, SI Lee Bioinformatics 36 (Supplement_2), i573-i582, 2020 | 64 | 2020 |
| Explainable machine learning prediction of synergistic drug combinations for precision cancer medicine JD Janizek, S Celik, SI Lee ICML Workshop on Computational Biology, 331769, 2018 | 57 | 2018 |
| An adversarial approach for the robust classification of pneumonia from chest radiographs JD Janizek, G Erion, AJ DeGrave, SI Lee Proceedings of the ACM conference on health, inference, and learning, 69-79, 2020 | 43 | 2020 |
| A cost-aware framework for the development of AI models for healthcare applications G Erion, JD Janizek, C Hudelson, RB Utarnachitt, AM McCoy, MR Sayre, ... Nature Biomedical Engineering 6 (12), 1384-1398, 2022 | 39* | 2022 |
| Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians AJ DeGrave, ZR Cai, JD Janizek, R Daneshjou, SI Lee Nature Biomedical Engineering, 1-13, 2023 | 36* | 2023 |
| Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models JD Janizek, AB Dincer, S Celik, H Chen, W Chen, K Naxerova, SI Lee Nature biomedical engineering 7 (6), 811-829, 2023 | 32* | 2023 |
| Learning Deep Attribution Priors Based On Prior Knowledge E Weinberger, J Janizek, SI Lee (NeurIPS 2020) Advances in Neural Information Processing Systems 33, 2019 | 31 | 2019 |
| PAUSE: principled feature attribution for unsupervised gene expression analysis JD Janizek, A Spiro, S Celik, BW Blue, JC Russell, TI Lee, M Kaeberlin, ... Genome Biology 24 (1), 81, 2023 | 14* | 2023 |
| Lab-bench: Measuring capabilities of language models for biology research JM Laurent, JD Janizek, M Ruzo, MM Hinks, MJ Hammerling, ... arXiv preprint arXiv:2407.10362, 2024 | 12 | 2024 |
| A deep profile of gene expression across 18 human cancers W Qiu, AB Dincer, JD Janizek, S Celik, M Pittet, K Naxerova, SI Lee bioRxiv, 2024 | | 2024 |
| Understanding Biomedical Machine Learning Models JD Janizek University of Washington, 2022 | | 2022 |
| Course Corrections for Clinical AI AJ DeGrave, JD Janizek, SI Lee Kidney360 2 (12), 2019-2023, 2021 | | 2021 |
| Characterization of the Lipase Stimulating Domain for Apolipoprotein AV and the Development of a Therapeutic Peptide for the Treatment of Hypertriglyceridemia JD Janizek, K Munro, MH Davidson, JB Ancsin Arteriosclerosis, Thrombosis, and Vascular Biology 35 (suppl_1), A537-A537, 2015 | | 2015 |
| A Novel Fluorescence-Based Assay is Used to Investigate the Triglyceride Hydrolytic Activity of Lipases and to Identify Synthetic Peptides With Strong Lipase-Stimulating Activities JD Janizek, K Munro, MH Davidson, JB Ancsin Arteriosclerosis, Thrombosis, and Vascular Biology 34 (suppl_1), A31-A31, 2014 | | 2014 |