Selected publications by Fred Hutch researchers that develop or apply deep learning methods.
2026
Jafari, O., Ma, S., Lam, B.D., Jiang, J.Y., Zhou, E., Ranjan, M., et al. (2026) Development and validation of venous thromboembolism-bidirectional encoder representations from transformers (VTE-BERT) natural language processing model. Journal of Thrombosis and Haemostasis.
Gong, G., Roychoudhury, S., Meisner, A., Pusztai, L., Goldberg, S.B., & Wei, W. (2026) LEAD-ONC: A Platform for Reconstructing Survival Data From Trial Reports for Targeted Evidence Synthesis and Clinical Trial Design. JCO Clinical Cancer Informatics.
Elia, M.V., Friesner, I.D., Kwon, D., Ni, L., Sinha, S., Ishiyama, Y., et al. (2026) Large Language Models and Adverse Event Detection Within Immunotherapy Clinical Trials. JAMA Network Open.
Templin, T., Song, S., Fort, S., & Sinnott-Armstrong, N. (2026) Participatory-informed preference optimization (PiPrO): A reinforcement learning simulation study. PLOS Digital Health.
Liu, L., Sathees, S., Sobel, A., Pepper, G., Greninger, A.L., Jerome, K.R., et al. (2026) BlotDx: A deep learning tool for Western blot-based diagnostics. Journal of Virological Methods.
Neidlinger, P., Lenz, T., Foersch, S., Loeffler, C.M.L., Clusmann, J., Gustav, M., et al. (2026) A deep learning framework for efficient pathology image analysis. Nature Communications.
Sujichantararat, S., Biswas, D., Kazerouni, A.S., Tsang, E.D., Sathe, A., Hippe, D.S., et al. (2026) Deep Learning-Based Synthetic Contrast-Enhanced Breast MRI for Monitoring Response to Neoadjuvant Therapy. Cancers.
Moon, J., Choi, M., Kim, Y., Rhee, H., Park, S.J., Kim, J.S., et al. (2026) Deep learning-based auto-segmentation and RECIST evaluation after concurrent chemoradiotherapy in locally advanced hepatocellular carcinoma patients. Frontiers in Oncology.
Alvarez-Michael, E., Peters, N., Schwengfelder, J., Zachow, D., Raschke, F., Löck, S., et al. (2026) Deep learning-based generation of direct stopping power ratio maps for MR-only proton therapy of primary brain tumor patients. Physics and Imaging in Radiation Oncology.
Chen, Z., Sayar, E., Guevara, D., Richards, H., Zhang, H., Patel, R.A., et al. (2026) Deep learning-based histologic classifiers enable molecular subtyping of metastatic prostate cancer. JCI Insight.
Gao, G., Yan, R., Song, A.H., Hsieh, H.C., Barner, L.A.E., Wang, F., et al. (2026) Deep-learning triage of three-dimensional pathology datasets for comprehensive and efficient pathologist assessments. Nature Biomedical Engineering.
Dacic, S., Shenker, D., Redman, M., Brunner, L., Saqi, A., Cooper, W.A., et al. (2026) Machine Learning Assessment of Pathologic Response in Lung Cancer Resections After Neoadjuvant Therapy-IASLC MPR Project. Journal of Thoracic Oncology.
Sankaranarayanan, A., Zhao, C., Hernandez, M.G., Clemens, E.A., Smythe, K.S., Kazerouni, A.S., et al. (2026) PhenoBIC: operator-free single-cell spatial phenotyping in multiplex imaging data using deep learning of cell staining patterns. bioRxiv.
Liang, J., Jiang, X., Reitsam, N.G., Lenz, T., Zhang, L., Gustav, M., et al. (2026) Spatial biomarker discovery via interpretable semantic learning in histopathology. Cancer Cell.
Lu, S.Z., Vermani, A., Sanno, K., Lu, J., Matsen, F.A., Jagota, M., et al. (2026) Conditionally Site-Independent Neural Evolution of Antibody Sequences. arXiv.
Patton, R.D., Netzley, A., Persse, T.W., Nair, A., Galipeau, P.C., Coleman, I.M., et al. (2026) Deep learning-based non-invasive profiling of tumor transcriptomes from cell-free DNA for precision oncology. bioRxiv.
Harel, N., Sung, K., Dumm, W., Johnson, M.M., Rich, D., Fukuyama, J., et al. (2026) Entrenchment of germline amino-acid differences in antibody affinity maturation. bioRxiv.
Visani, G.M., Galvin, W., Jones, Z., Pun, M.N., Daniel, E., Borisiak, K., et al. (2026) HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction. bioRxiv.
Matsen, F.A., Dumm, W., Sung, K., Johnson, M.M., Rich, D.H., Starr, T.N., et al. (2026) Separating selection from mutation in antibody language models. eLife.
Lin, Z., Gao, Y., & Sun, W. (2026) Supervised deep learning with gene functional annotation for cell classification. PLoS Computational Biology.
Collienne, L., Richman, H., Rich, D.H., Barker, M., Jennings-Shaffer, C., & Matsen Iv, F.A. (2026) Unifying Phylogenetic Traversal and Deep Learning to Guide Tree Exploration. Systematic Biology.
Ralph, D.K., Bakis, A.G., Galloway, J.G., Vora, A.A., Araki, T., Victora, G.D., et al. (2026) Inference of germinal center evolutionary dynamics via simulation-based deep learning. eLife.
2025
Zhai, G., Bar, M., Cowan, A.J., Rubinstein, S., Shi, Q., Zhang, N., et al. (2025) AI for evidence-based treatment recommendation in oncology: a blinded evaluation of large language models and agentic workflows. Frontiers in Artificial Intelligence.
Nagarajan, R., Klotzman, V., Kondo, M., Godambe, S., Gold, A., Henderson, J., et al. (2025) Taxonomy Portraits: Deciphering the Hierarchical Relationships of Medical Large Language Models. JMIR Medical Informatics.
Peters, N., Haneda, E., Zhang, J., Karageorgos, G., Xia, W., Verburg, J., et al. (2025) A hybrid training database and evaluation benchmark for assessing metal artifact reduction methods for X-ray CT imaging. Medical Physics.
Christie, J.R., Eddy, K., Malthaner, R.A., Qiabi, M., Verma, S., Breadner, D., et al. (2025) Assessing clinician performance using a multi-modality clinical decision-support system for lung cancer prognostication. Scientific Reports.
Khan, S.A., Faerber, D., Kirkey, D., Raffel, S., Hadland, B., Deininger, M., et al. (2025) Cross-Species Morphology Learning Enables Nucleic Acid-Independent Detection of Live Mutant Blood Cells. bioRxiv.
Ercan, C., Pan, X., Paulson, T.G., Stachler, M.D., Akarca, F.G., Grady, W.M., et al. (2025) Histopathology-based Spatial Profiling of Immune and Molecular Features Predicts Cancer risk in Barrett's Esophagus. medRxiv.
Li, F., Liu, S., & Sun, W. (2025) Improved Deep Learning Prediction of TCR-HLA Associations. Statistics in Biosciences.
Johnson, M.M., Sung, K., Haddox, H.K., Vora, A.A., Araki, T., Victora, G.D., et al. (2025) Nucleotide context models outperform protein language models for predicting antibody affinity maturation. PLoS Computational Biology.
Visani, G.M., Pun, M.N., Minervina, A.A., Bradley, P., Thomas, P.G., & Nourmohammad, A. (2025) T cell receptor specificity landscape revealed through de novo peptide design. Proceedings of the National Academy of Sciences of the United States of America.
Liao, N., Li, C., Gradishar, W.J., Klimberg, V.S., Roshal, J.A., Yuan, T., et al. (2025) Accuracy and Reproducibility of ChatGPT Responses to Breast Cancer Tumor Board Patients. JCO Clinical Cancer Informatics.
Nickel, B., Ayre, J., Marinovich, M.L., Smith, D.P., Chiam, K., Lee, C.I., et al. (2025) Are AI chatbots concordant with evidence-based cancer screening recommendations?. Patient Education and Counseling.
Krawczuk, P., Fox, Z.R., Petkov, V., Negoita, S., Doherty, J., Stroup, A., et al. (2025) Large-scale deep learning for metastasis detection in pathology reports. JAMIA Open.
Rubinstein, S., Mohsin, A., Banerjee, R., Ma, W., Mishra, S., Kwok, M., et al. (2025) Summarizing clinical evidence utilizing large language models for cancer treatments: a blinded comparative analysis. Frontiers in Digital Health.
Gustav, M., van Treeck, M., Reitsam, N.G., Carrero, Z.I., Loeffler, C.M.L., Rabasco Meneghetti, A., et al. (2025) Assessing genotype-phenotype correlations in colorectal cancer with deep learning: a multicentre cohort study. The Lancet. Digital Health.
Oviedo, F., Kazerouni, A.S., Liznerski, P., Xu, Y., Hirano, M., Vandermeulen, R.A., et al. (2025) Cancer Detection in Breast MRI Screening via Explainable AI Anomaly Detection. Radiology.
Orouskhani, M., Rauniyar, S., Morella, N., Lachance, D., Minot, S.S., & Dey, N. (2025) Deep learning imaging analysis to identify bacterial metabolic states associated with carcinogen production. Discover Imaging.
Ahmed, S.R., Befano, B., Egemen, D., Rodriguez, A.C., Desai, K.T., Jeronimo, J., et al. (2025) Generalizable deep neural networks for image quality classification of cervical images. Scientific Reports.
Sankaranarayanan, A., Khachaturov, G., Smythe, K.S., & Mittal, S. (2025) Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies. Communications Biology.
Wu, E., Bieniosek, M., Wu, Z., Thakkar, N., Charville, G.W., Makky, A., et al. (2025) ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images. Nature Communications.
Motmaen, A., Jude, K.M., Wang, N., Minervina, A., Feldman, D., Lichtenstein, M.A., et al. (2025) Targeting peptide-MHC complexes with designed T cell receptors and antibodies. bioRxiv.
Christie, J.R., Romine, P., Eddy, K., Chen, D.L., Daher, O., Abdelrazek, M., et al. (2025) Thorax-encompassing multi-modality PET/CT deep learning model for resected lung cancer prognostication: A retrospective, multicenter study. Medical Physics.
Matsen, F.A., Sung, K., Johnson, M.M., Dumm, W., Rich, D., Starr, T.N., et al. (2025) A Sitewise Model of Natural Selection on Individual Antibodies via a Transformer-Encoder. Molecular Biology and Evolution.
Yang, X., Zhao, F., Ren, T., Chen, C., Byrne, K.T., Danilov, A.V., et al. (2025) OmicsTweezer: A distribution-independent cell deconvolution model for multi-omics Data. Cell Genomics.
Sung, K., Johnson, M.M., Dumm, W., Simon, N., Haddox, H., Fukuyama, J., et al. (2025) Thrifty wide-context models of B cell receptor somatic hypermutation. eLife.
Salerno, S., & Li, Y. (2025) A Pseudo-Value Approach to Causal Deep Learning of Semi-Competing Risks. Arabian Journal of Mathematics.
2024
Bricker, J.B., Sullivan, B., Mull, K., Santiago-Torres, M., & Lavista Ferres, J.M. (2024) Conversational Chatbot for Cigarette Smoking Cessation: Results From the 11-Step User-Centered Design Development Process and Randomized Controlled Trial. JMIR MHealth and UHealth.
Peluso, A., Danciu, I., Yoon, H.J., Yusof, J.M., Bhattacharya, T., Spannaus, A., et al. (2024) Deep learning uncertainty quantification for clinical text classification. Journal of Biomedical Informatics.
Edwards, L.A., Yang, C., Sharma, S., Chen, Z.H., Gorantla, L., Joshi, S.A., et al. (2024) Building a machine learning-assisted echocardiography prediction tool for children at risk for cancer therapy-related cardiomyopathy. Cardio-oncology.
Sui, Z., Li, Z., & Sun, W. (2024) Exploit Spatially Resolved Transcriptomic Data to Infer Cellular Features from Pathology Imaging Data. bioRxiv.
Kim, J.G., Haslam, B., Diab, A.R., Sakhare, A., Grisot, G., Lee, H., et al. (2024) Impact of a Categorical AI System for Digital Breast Tomosynthesis on Breast Cancer Interpretation by Both General Radiologists and Breast Imaging Specialists. Radiology. Artificial Intelligence.
Roche, S.D., Ekwunife, O.I., Mendonca, R., Kwach, B., Omollo, V., Zhang, S., et al. (2024) Measuring the performance of computer vision artificial intelligence to interpret images of HIV self-testing results. Frontiers in Public Health.
Han, S., Phasouk, K., Zhu, J., & Fong, Y. (2024) Optimizing deep learning-based segmentation of densely packed cells using cell surface markers. BMC Medical Informatics and Decision Making.
Sinicrope, F.A., Nelson, G.D., Saberzadeh-Ardestani, B., Segovia, D.I., Graham, R.P., Wu, C., et al. (2024) Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence. Cancer Research Communications.
Vadathya, A.K., Garza, T., Alam, U., Ho, A., Musaad, S.M.A., Beltran, A., et al. (2024) Validation studies of the FLASH-TV system to passively measure children's TV viewing. Scientific Reports.
Liu, B., Greenwood, N.F., Bonzanini, J.E., Motmaen, A., Sharp, J., Wang, C., et al. (2024) Design of high specificity binders for peptide-MHC-I complexes. bioRxiv.
Pun, M.N., Ivanov, A., Bellamy, Q., Montague, Z., LaMont, C., Bradley, P., et al. (2024) Learning the shape of protein microenvironments with a holographic convolutional neural network. Proceedings of the National Academy of Sciences of the United States of America.
Pineda-Antunez, C., Seguin, C., van Duuren, L.A., Knudsen, A.B., Davidi, B., Nascimento de Lima, P., et al. (2024) Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models. Medical Decision Making.
2023
Liu, S., Bradley, P., & Sun, W. (2023) Neural network models for sequence-based TCR and HLA association prediction. PLoS Computational Biology.
Tolkach, Y., Ovtcharov, V., Pryalukhin, A., Eich, M.L., Gaisa, N.T., Braun, M., et al. (2023) An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading. NPJ Precision Oncology.
Erion Barner, L.A., Gao, G., Reddi, D.M., Lan, L., Burke, W., Mahmood, F., et al. (2023) Artificial Intelligence-Triaged 3-Dimensional Pathology to Improve Detection of Esophageal Neoplasia While Reducing Pathologist Workloads. Modern Pathology.
Milewski, D., Jung, H., Brown, G.T., Liu, Y., Somerville, B., Lisle, C., et al. (2023) Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group. Clinical Cancer Research.
Visani, G.M., Galvin, W., Pun, M.N., & Nourmohammad, A. (2023) H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing. arXiv.
Motmaen, A., Dauparas, J., Baek, M., Abedi, M.H., Baker, D., & Bradley, P. (2023) Peptide-binding specificity prediction using fine-tuned protein structure prediction networks. Proceedings of the National Academy of Sciences of the United States of America.
Bradley, P. (2023) Structure-based prediction of T cell receptor:peptide-MHC interactions. eLife.
2022
Zhou, L.Y., Zou, F., & Sun, W. (2022) Prioritizing candidate peptides for cancer vaccines through predicting peptide presentation by HLA-I proteins. Biometrics.
Yoon, H.J., Peluso, A., Durbin, E.B., Wu, X.C., Stroup, A., Doherty, J., et al. (2022) Automatic information extraction from childhood cancer pathology reports. JAMIA Open.
De Angeli, K., Gao, S., Danciu, I., Durbin, E.B., Wu, X.C., Stroup, A., et al. (2022) Class imbalance in out-of-distribution datasets: Improving the robustness of the TextCNN for the classification of rare cancer types. Journal of Biomedical Informatics.
Yoon, H.J., Stanley, C., Christian, J.B., Klasky, H.B., Blanchard, A.E., Durbin, E.B., et al. (2022) Optimal vocabulary selection approaches for privacy-preserving deep NLP model training for information extraction and cancer epidemiology. Cancer Biomarkers.
De Angeli, K., Gao, S., Blanchard, A., Durbin, E.B., Wu, X.C., Stroup, A., et al. (2022) Using ensembles and distillation to optimize the deployment of deep learning models for the classification of electronic cancer pathology reports. JAMIA Open.
Hsu, W., Hippe, D.S., Nakhaei, N., Wang, P.C., Zhu, B., Siu, N., et al. (2022) External Validation of an Ensemble Model for Automated Mammography Interpretation by Artificial Intelligence. JAMA Network Open.
Kang, Y., Vijay, S., & Gujral, T.S. (2022) Deep neural network modeling identifies biomarkers of response to immune-checkpoint therapy. IScience.
Wang, A., Hai, R., Rider, P.J., & He, Q. (2022) Noncoding RNAs and Deep Learning Neural Network Discriminate Multi-Cancer Types. Cancers.
Bellettiere, J., Nakandala, S., Tuz-Zahra, F., Winkler, E.A.H., Hibbing, P.R., Healy, G.N., et al. (2022) CHAP-Adult: A Reliable and Valid Algorithm to Classify Sitting and Measure Sitting Patterns Using Data From Hip-Worn Accelerometers in Adults Aged 35. Journal for the Measurement of Physical Behaviour.
Isacchini, G., Spisak, N., Nourmohammad, A., Mora, T., & Walczak, A.M. (2022) Mutual information maximization for amortized likelihood inference from sampled trajectories: MINIMALIST. Physical Review. E.
2021
Nguyen, P., Chien, S., Dai, J., Monnat, R.J., Becker, P.S., & Kueh, H.Y. (2021) Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies. PLoS Computational Biology.
Humphreys, I.R., Pei, J., Baek, M., Krishnakumar, A., Anishchenko, I., Ovchinnikov, S., et al. (2021) Computed structures of core eukaryotic protein complexes. Science.
Ling, W., Qi, Y., Hua, X., & Wu, M.C. (2021) Deep ensemble learning over the microbial phylogenetic tree (DeepEn-Phy). Proceedings. IEEE International Conference on Bioinformatics and Biomedicine.
2020
Wang, S., McCormick, T. H., & Leek, J. T. (2020) Methods for correcting inference based on outcomes predicted by machine learning. Proceedings of the National Academy of Sciences, 117(48), 30266-30275.
Fong, Y, Xu, J. (2020) Forward stepwise deep autoencoder-based monotone nonlinear dimensionality reduction methods, Journal of Computational and Graphical Statistics, 30(3):519-529
Quon, J.L., Bala, W., Chen, L.C., Wright, J., Kim, L.H., Han, M., et al. (2020) Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study. AJNR. American Journal of Neuroradiology.
Schaffter, T., Buist, D.S.M., Lee, C.I., Nikulin, Y., Ribli, D., Guan, Y., et al. (2020) Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA Network Open.
Duan, W., Zhang, J., Zhang, L., Lin, Z., Chen, Y., Hao, X., et al. (2020) Evaluation of an artificial intelligent hydrocephalus diagnosis model based on transfer learning. Medicine.
Erijman, A., Kozlowski, L., Sohrabi-Jahromi, S., Fishburn, J., Warfield, L., Schreiber, J., et al. (2020) A High-Throughput Screen for Transcription Activation Domains Reveals Their Sequence Features and Permits Prediction by Deep Learning. Molecular Cell.
Isacchini, G., Sethna, Z., Elhanati, Y., Nourmohammad, A., Walczak, A.M., & Mora, T. (2020) Generative models of T-cell receptor sequences. Physical Review. E.
Vijay, S., & Gujral, T.S. (2020) Non-linear Deep Neural Network for Rapid and Accurate Prediction of Phenotypic Responses to Kinase Inhibitors. IScience.
Luedtke, A., Carone, M., Simon, N., & Sofrygin, O. (2020) Learning to learn from data: Using deep adversarial learning to construct optimal statistical procedures. Science Advances.