Research and Resources

Our research showcases how AI and machine learning are transforming precision medicine. Explore recent advancements in oncology, chronic disease management, and real-world data innovation—presented at leading conferences and developed in collaboration with top academic and clinical partners.

Research powered by ORIEN® and the Total Cancer Care® study can be viewed on Aster Insights™ Publications Page. Zephyr AI now extends this legacy, providing deep multimodal oncology data for discovery and validation.

Evaluation of a Novel Machine Learning Method for PARP Inhibitor Sensitivity Prediction Using Real-World Data

ASCO 2024 Annual Meeting | May 31 - June 4, 2024 | Chicago, IL

This study demonstrates Zephyr AI’s multi-modal machine learning method for identifying late-stage ovarian cancer patients likely to respond to olaparib, independent of HRD status. Our model significantly outperformed conventional biomarker approaches, improving real-world survival outcomes and offering biological insights through our Vulnerability Networks™

J Clin Oncol 42, 2024 (suppl 16; abstr 5583)

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Generative Bayesian Networks for Augmentation of Molecular Data from Commercial Genetic Panels

AACR 2024 Annual Meeting | April 5–10, 2024 | San Diego, CA

Zephyr AI’s generative Bayesian network approach synthesizes comprehensive molecular profiles from limited NGS panel data in lung and breast cancer. This method enhances sparse tumor data to enable downstream analysis, biomarker discovery, and advanced ML model development without additional testing, bridging a major gap in real-world precision oncology.

Abstract #7373

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Reconstructing a Latent Representation of Gene Expression from Genomic Alterations to Improve Clinical Utility of Real-World Clinicogenomics Data

AACR 2024 Annual Meeting | April 5–10, 2024 | San Diego, CA

Zephyr AI’s Mut2Ex model reconstructs tumor gene expression profiles using only genetic data from commercial NGS panels combined with minimal clinical information. Trained on ~1,200 DepMap cell lines, Mut2Ex generated expression profiles for ~10,000 TCGA and ~180,000 GENIE tumors, substantially enhancing the clinical utility of real-world clinicogenomics data for precision medicine applications.

Abstract #3519

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ZephyrAI SITC Poster TME from NGS panels

Reconstructing Gene Expression from Clinical and Genetic Panel Data for Predictions of Tumor Microenvironment Features and Response to Immune Checkpoint Inhibitor Therapy

SITC 2023 Annual Meeting | November 1–5, 2023 | San Diego, CA

Zephyr AI developed a machine learning model that reconstructs tumor gene expression from NGS panel data to predict tumor microenvironment (TME) features critical for immune checkpoint inhibitor (ICI) response. Trained on over 8,000 tumors across 32 cancer types, the model delivers high reconstruction accuracy and supports personalized immunotherapy strategies using real-world clinical data.

Journal for Immunotherapy of Cancer (JITC) preprint. Abstract #1296

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ZephyrAI SITC Poster ImmunoBERT ICI model

Integrating Drug Structure and Target Binding Affinity for Improved Prediction of Survival in Cancer Patients Treated with Immune Checkpoint Inhibitors

SITC 2023 Annual Meeting | November 1–5, 2023 | San Diego, CA

This research introduces ImmunoBERT, Zephyr AI’s machine learning model that predicts survival in cancer patients receiving immune checkpoint inhibitors. Integrating drug structure, target binding profiles, and NGS panel data from 1,700 patients, ImmunoBERT outperformed top DREAM challenge submissions and reconstructs key tumor microenvironment features, enabling personalized immunotherapy selection without specialized testing.

Journal for Immunotherapy of Cancer (JITC) preprint. Abstract #1296

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Predicting Sensitivity to Tagrisso in NSCLC Using Systems Biology-Informed Machine Learning

ASCO 2023 Annual Meeting | June 4-6, 2023 | Chicago, IL

Zephyr AI developed a systems biology-informed machine learning model to predict sensitivity to Tagrisso (a third-generation TKI) in NSCLC using clinically available NGS data. Validated across NSCLC cell lines, PDX models, and a real-world cohort (n=334), the model identifies pharmacological vulnerabilities and reveals a potential opportunity for label expansion beyond current FDA indications.

J Clin Oncol 41, 2023 (suppl 16; abstr 3136)

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Empowering Clinicians to Predict and Prevent Adverse Outcomes in Type 2 Diabetes Using Transparent AI

SAIL 2023 Annual Meeting | May 9-12, 2023 | Río Grande, Puerto Rico

Zephyr AI, in collaboration with MedStar Health, developed interpretable AI/ML models that predict 0–5 year risk of adverse outcomes in type 2 diabetes using real-world EHR, claims, and social determinants data from over one million patients. The models deliver strong performance (AUC ~0.85) and provide actionable, transparent predictions to support earlier clinical interventions.

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