Abstract Scientific progress increasingly relies on large, complex biomedical datasets; data management concerns (heterogeneity, volume, sensitivity, and a pervasive lack of standardization) present significant challenges, especially in the context of AI adoption and precision medicine. This fragmented data landscape l...
C. Chute, A. Bahmani, Alex H. Wagner et al.· Journal of Clinical and Tran...· 0 citations
Cancer genomic data sharing is a core pillar of precision oncology, underpinning reproducibility, scalability, and equity. The ability to discover, access, integrate, interpret, and reuse cancer 'omics data is a key determinant of precision oncology's value and benefit at scale. This work has been conducted as part of...
D. Torrents, Oliver Hofmann, Karen A. Cranston et al.· EBioMedicine· 1 citation
The Commission developed a value-based resource-stratified implementation framework, a system-readiness assessment tool, a precision oncology workforce competency-based framework, a precision oncology monitoring and evaluation framework, and principles for responsible data sharing and health-system learning.
R. Casolino, J. Mateo, E. D. de Vries et al.· The Lancet Oncology· 11 citations
Abstract Summary The druggable genome encompasses the genes that are known or predicted to interact with drugs. The Drug-Gene Interaction Database (DGIdb) provides an integrated resource for discovering and contextualizing these interactions, supporting a broad range of research and clinical applications. DGIdb is curr...
Lars Schimmelpfennig, M. Cannon, Quentin Cody et al.· Bioinformatics· 0 citations
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