Barzilay, Regina

A Deep Learning Approach to Antibiotic Discovery

Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. (2020) · Cell

Computational Drug Discovery MethodsMicrobial Natural Products and Biosynthesis

Cited by 2,463

Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models

Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2022) · Nature

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 2,369

De novo design of protein structure and function with RFdiffusion

Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2023) · Nature

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 2,325

Analyzing Learned Molecular Representations for Property Prediction

Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., et al. (2019) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 1,923

Rationalizing Neural Predictions

Lei, T., Barzilay, R., & Jaakkola, T. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

Explainable Artificial Intelligence (XAI)Sentiment Analysis and Opinion Mining

Cited by 777

A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction

Yala, A., Lehman, C., Schuster, T., Portnoi, T., & Barzilay, R. (2019) · Radiology

Digital Radiography and Breast ImagingAI in cancer detection

Cited by 758

A graph-convolutional neural network model for the prediction of chemical reactivity

Coley, C. W., Jin, W., Rogers, L., Jamison, T. F., Jaakkola, T. S., Green, W. H., Barzilay, R., & Jensen, K. F. (2019) · Chemical Science

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 679

Convolutional embedding of attributed molecular graphs for physical property prediction

Coley, C. W., Barzilay, R., Green, W. H., Jaakkola, T. S., & Jensen, K. F. (2017) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsGraph theory and applications

Cited by 470

Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation

Lehman, C. D., Yala, A., Schuster, T., Dontchos, B., Bahl, M., Swanson, K., & Barzilay, R. (2019) · Radiology

Digital Radiography and Breast ImagingAI in cancer detection

Cited by 294

Sybil: a validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography

Mikhael, P. G., Wohlwend, J., Yala, A., Karstens, L., Xiang, J., Takigami, A. K., Bourgouin, P. P., Chan, P., Mrah, S., Amayri, W., et al. (2023) · Journal of Clinical Oncology

AI in cancer detectionLung Cancer Diagnosis and Treatment

Cited by 288

Toward robust mammography-based models for breast cancer risk

Yala, A., Mikhael, P. G., Strand, F., Lin, G., Smith, K., Wan, Y.-L., Lamb, L., Hughes, K., Lehman, C., & Barzilay, R. (2021) · Science Translational Medicine

Digital Radiography and Breast ImagingAI in cancer detection

Cited by 287

A Deep Learning Model to Triage Screening Mammograms: A Simulation Study

Yala, A., Schuster, T., Miles, R., Barzilay, R., & Lehman, C. (2019) · Radiology

AI in cancer detectionDigital Radiography and Breast Imaging

Cited by 210

Multi-institutional validation of a mammography-based breast cancer risk model

Yala, A., Mikhael, P. G., Strand, F., Lin, G., Satuluru, S., Kim, T., Banerjee, I., Gichoya, J., Trivedi, H., Lehman, C. D., et al. (2022) · Journal of Clinical Oncology

AI in cancer detectionDigital Radiography and Breast Imaging

Cited by 204

Towards Debiasing Fact Verification Models

Schuster, T., Shah, D., Yeo, Y. J. S., Roberto Filizzola Ortiz, D., Santus, E., & Barzilay, R. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Misinformation and Its ImpactsTopic Modeling

Cited by 171

Multi-Source Domain Adaptation with Mixture of Experts

Guo, J., Shah, D., & Barzilay, R. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Domain Adaptation and Few-Shot LearningSentiment Analysis and Opinion Mining

Cited by 168

Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence

Schuster, T., Fisch, A., & Barzilay, R. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Misinformation and Its ImpactsTopic Modeling

Cited by 126

The limitations of stylometry for detecting machine-generated fake news

Schuster, T., Schuster, R., Shah, D. J., & Barzilay, R. (2020) · Computational Linguistics

Misinformation and Its ImpactsAuthorship Attribution and Profiling

Cited by 109

Optimizing risk-based breast cancer screening policies with reinforcement learning

Yala, A., Mikhael, P. G., Lehman, C., Lin, G., Strand, F., Wan, Y.-L., Hughes, K., Satuluru, S., Kim, T., Banerjee, I., et al. (2022) · Nature Medicine

AI in cancer detectionGlobal Cancer Incidence and Screening

Cited by 103

Protein codes promote selective subcellular compartmentalization

Kilgore, H. R., Chinn, I., Mikhael, P. G., Mitnikov, I., Van Dongen, C., Zylberberg, G., Afeyan, L., Banani, S. F., Wilson-Hawken, S., Lee, T. I., et al. (2025) · Science

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 99

Protein codes promote selective subcellular compartmentalization

Kilgore, H. R., Chinn, I., Mikhael, P. G., Mitnikov, I., Van Dongen, C., Zylberberg, G., Afeyan, L., Banani, S., Wilson-Hawken, S., Lee, T. I., et al. (2024) · Science

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 99

Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem

Trippe, B. L., Yim, J., Tischer, D., Baker, D., Broderick, T., Barzilay, R., & Jaakkola, T. (2022) · arXiv

Protein Structure and DynamicsComputational Drug Discovery Methods

Cited by 92

Machine Learning Methods to Extract Documentation of Breast Cancer Symptoms From Electronic Health Records

Forsyth, A. W., Barzilay, R., Hughes, K. S., Lui, D., Lorenz, K. A., Enzinger, A., Tulsky, J. A., & Lindvall, C. (2018) · Journal of Pain and Symptom Management

Machine Learning in HealthcareBiomedical Text Mining and Ontologies

Cited by 83

Automatic analysis of medical dialogue in the home hemodialysis domain: Structure induction and summarization

Lacson, R. C., Barzilay, R., & Long, W. J. (2006) · Journal of Biomedical Informatics

Speech and dialogue systemsBiomedical Text Mining and Ontologies

Cited by 38

Evaluation of a prototype machine learning tool to semi-automate data extraction for systematic literature reviews

Panayi, A., Ward, K., Benhadji-Schaff, A., Ibanez-Lopez, A. S., Xia, A., & Barzilay, R. (2023) · Systematic Reviews

Biomedical Text Mining and OntologiesMeta-analysis and systematic reviews

Cited by 23