Past CBIIT Events
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An Intro to Gene Set Enrichment Analysis and the Molecular Signatures Database
Attend this webinar to learn more about an NCI-funded computer model and genomics resource that can help you explore gene expression changes in cancer.
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Federated Foundational Models: 2026 AACR Annual Meeting
CBIIT staff describe how NCI’s Federated Learning-based AI in Modal Exportation (FLAIMME) consortium can help cancer centers share AI models while still retaining local data control and institutional governance.
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Modernizing Clinical Trial Communication: 2026 AACR Annual Meeting
NCI CBIIT staff demonstrate three AI tools for consent form generation, plain-language trial summaries, and Spanish language translation. These tools should cut down document development time and expand clinical trial access for diverse populations.
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Cancer Research Data Commons and Pediatric Research: 2026 AACR Annual Meeting
NCI staff showcase how the Cancer Research Data Commons ecosystem is stronger thanks to partnerships with ARPA-H’s Biomedical Data Fabric and NCI’s Childhood Cancer Data Initiative, highlighting technologies that harmonize semantics, integrate ontology, and enable artificial intelligence.
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Validating AI in Digital Pathology for Clinical Use Beyond the Algorithm
On April 15, the Data Science Seminar Series will introduce the challenges and successes of developing standardized artificial intelligence models in digital pathology for cancer diagnostics.
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Foundation Models for Cancer Workshop
On March 26, NCI CBIIT staff, Drs. Shannon Silkensen and Umit Topaloglu, will highlight how an NCI CBIIT-led federated learning network can give researchers the space to build robust foundational models.
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Multi-modal Modeling in Precision Medicine: From Data Imputation to Synthetic Data
On March 18, the Data Science Seminar Series will introduce the concept of cross-modal data modeling—a methodology that uses foundation models to ascribe missing biomedical research data and generate realistic synthetic samples.
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Data Driven QSP Modeling of Cancer: A Step Toward Personalized Treatment
Join this webinar on March 6, 2026, at 11 a.m. to learn how an NCI-funded data-driven QSP model could help predict cancer treatment response.
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Robotics, Agents, and World (RAW) Models to Target Cancer
Discover how artificial intelligence (AI), robotics, and predictive models could transform cancer drug development. During this Data Science Seminar, Dr. Arvind Ramanathan will present an approach that uses automated systems to generate and test hypotheses to design new cancer therapies.
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XNAT Scout: Enabling Translational AI
Attend this webinar to learn more about XNAT Scout—a new extension of the XNAT imaging informatics platform that’s designed to close the gap between artificial intelligence (AI) model development and clinical deployment.