Ben Lengerich
Ben Lengerich
MIT, Broad Institute
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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
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
Precision lasso: Accounting for correlations and linear dependencies in high-dimensional genomic data
H Wang, BJ Lengerich, B Aragam, EP Xing
Bioinformatics, 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
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
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
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
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
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
Personalized Regression Enables Sample-Specific Pan-Cancer Analysis
BJ Lengerich, B Aragam, EP Xing
Bioinformatics 34 (13), i178-i186, 2018
Learning sample-specific models with low-rank personalized regression
B Lengerich, B Aragam, EP Xing
Advances in Neural Information Processing Systems 32, 2019
Dropout as a regularizer of interaction effects
BJ Lengerich, E Xing, R Caruana
International Conference on Artificial Intelligence and Statistics, 7550-7564, 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
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
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
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
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
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
Data Science with LLMs and Interpretable Models
S Bordt, B Lengerich, H Nori, R Caruana
arXiv preprint arXiv:2402.14474, 2024
Contextualized machine learning
B Lengerich, CN Ellington, A Rubbi, M Kellis, EP Xing
arXiv preprint arXiv:2310.11340, 2023
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Artículos 1–20