Identification of resistance mechanisms to EGFR treatment in the real world using a clinicogenomic database Published April 2018 Citation Singal, G, Li, G, Agarwala, V, Kaushik, G, O’Connell, C, Alpha-CobbG, Caron, T, Bourque, D, Guria, A, Frank, S, Frampton, G, Carson, K, Abernethy, A.P., Miller, V.A. . AACR Annual Meeting. . https://4076230.fs1.hubspotusercontent-na1.net/hubfs/4076230/flatiron-com-pdf/AACR-2018-FH-FMI-CGDB-Poster-180411.pdf Authors: Sources:American Association for Cancer Research Annual Meeting Share Posted inPublicationsDrug discovery More publicationsAACR Special Conference in Cancer Research: Artificial Intelligence and Machine LearningJuly 2025Using large language models for scalable extraction of real-world progression events across multiple cancer typesCohen A, Krismer K, Magee K, et al. Real-world evidencePublication summaryPublicationsData scienceMachine learningArtificial intelligenceAACR Special Conference in Cancer Research: Artificial Intelligence and Machine LearningJuly 2025Fairness by design: End-to-end bias evaluation for LLM-generated dataEstevez M, Mbah O, Sheikh A, et al.Publication summaryPublicationsData scienceHealth equityBreast cancerArtificial intelligenceJAMA OncologyJuly 2025Clinical outcomes of perioperative immunotherapy in resectable non–small cell lung cancerDesai A, Schwed K, Kalesinskas L, et al. Publication summaryPublicationsTreatment patternsNon-small cell lung cancer