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Retrospective multi-cohort validation of a real-world transcriptomics-guided machine learning model for treatment response prediction in breast cancer
2026-01-22
oncology
Title + abstract only
View on medRxiv
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Selection of systemic therapy for breast cancer remains largely empirical, particularly for chemotherapy, due to the lack of robust biomarkers that predict treatment response at the individual patient level. We developed Oncology CoPilot, a real-world, transcriptomics-guided machine learning (ML) decision-support model designed to integrate heterogeneous tumor gene expression data and treatment response annotations to support treatment response stratification across therapeutic classes. Oncology...
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