Opportunities and obstacles for deep learning in biology and medicine T Ching, DS Himmelstein, BK Beaulieu-Jones, AA Kalinin, BT Do, ... Journal of the Royal Society Interface 15 (141), 2018 | 2025 | 2018 |
Neural additive models: Interpretable machine learning with neural nets R Agarwal, L Melnick, N Frosst, X Zhang, B Lengerich, R Caruana, ... Advances in neural information processing systems 34, 4699-4711, 2021 | 412 | 2021 |
Precision lasso: Accounting for correlations and linear dependencies in high-dimensional genomic data H Wang, BJ Lengerich, B Aragam, EP Xing Bioinformatics, 2018 | 143 | 2018 |
How interpretable and trustworthy are gams? CH Chang, S Tan, B Lengerich, A Goldenberg, R Caruana Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 66 | 2021 |
Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations BJ Lengerich, AL Maas, C Potts International Conference on Computational Linguistics (COLING) 27, 2423-2436, 2018 | 37 | 2018 |
Purifying interaction effects with the functional anova: An efficient algorithm for recovering identifiable additive models B Lengerich, S Tan, CH Chang, G Hooker, R Caruana International Conference on Artificial Intelligence and Statistics, 2402-2412, 2020 | 34 | 2020 |
Ten quick tips for deep learning in biology BD Lee, A Gitter, CS Greene, S Raschka, F Maguire, AJ Titus, MD Kessler, ... PLoS computational biology 18 (3), e1009803, 2022 | 26 | 2022 |
Towards visual explanations for convolutional neural networks via input resampling BJ Lengerich, S Konam, EP Xing, S Rosenthal, M Veloso arXiv preprint arXiv:1707.09641, 2017 | 26 | 2017 |
Experimental and computational mutagenesis to investigate the positioning of a general base within an enzyme active site JP Schwans, P Hanoian, BJ Lengerich, F Sunden, A Gonzalez, Y Tsai, ... Biochemistry 53 (15), 2541-2555, 2014 | 23 | 2014 |
Personalized Regression Enables Sample-Specific Pan-Cancer Analysis BJ Lengerich, B Aragam, EP Xing Bioinformatics 34 (13), i178-i186, 2018 | 22 | 2018 |
Learning sample-specific models with low-rank personalized regression B Lengerich, B Aragam, EP Xing Advances in Neural Information Processing Systems 32, 2019 | 21 | 2019 |
Dropout as a regularizer of interaction effects BJ Lengerich, E Xing, R Caruana International Conference on Artificial Intelligence and Statistics, 7550-7564, 2022 | 17* | 2022 |
Automated interpretable discovery of heterogeneous treatment effectiveness: A COVID-19 case study BJ Lengerich, ME Nunnally, Y Aphinyanaphongs, C Ellington, R Caruana Journal of biomedical informatics 130, 104086, 2022 | 8 | 2022 |
Using interpretable machine learning to predict maternal and fetal outcomes TM Bosschieter, Z Xu, H Lan, BJ Lengerich, H Nori, K Sitcov, V Souter, ... arXiv preprint arXiv:2207.05322, 2022 | 7 | 2022 |
LLMs understand glass-box models, discover surprises, and suggest repairs BJ Lengerich, S Bordt, H Nori, ME Nunnally, Y Aphinyanaphongs, ... arXiv preprint arXiv:2308.01157, 2023 | 4 | 2023 |
Death by Round Numbers: Glass-Box Machine Learning Uncovers Biases in Medical Practice BJ Lengerich, R Caruana, ME Nunnally, M Kellis medRxiv, 2022.04. 30.22274520, 2022 | 4* | 2022 |
Discriminative subtyping of lung cancers from histopathology images via contextual deep learning BJ Lengerich, M Al-Shedivat, A Alavi, J Williams, S Labbaki, EP Xing medRxiv, 2020.06. 25.20140053, 2020 | 3 | 2020 |
Interpretable predictive models to understand risk factors for maternal and fetal outcomes TM Bosschieter, Z Xu, H Lan, BJ Lengerich, H Nori, I Painter, V Souter, ... Journal of Healthcare Informatics Research 8 (1), 65-87, 2024 | 2 | 2024 |
Contextualized machine learning B Lengerich, CN Ellington, A Rubbi, M Kellis, EP Xing arXiv preprint arXiv:2310.11340, 2023 | 2 | 2023 |
Understanding risk factors for shoulder dystocia using interpretable machine learning H Lan, TM Bosschieter, Z Xu, B Lengerich, H Nori, K Sitcov, I Painter, ... American Journal of Obstetrics & Gynecology 228 (1), S753, 2023 | 2 | 2023 |