The Quantitative Sciences Division is the hub for quantitative research in the Department of Oncology at the Sidney Kimmel Comprehensive Cancer Center (SKCCC). The digital age of clinical trials, patient data, high-throughput molecular and imaging technologies, and health information systems is ushering in a new age of cancer science. Division members work together with clinical investigators to transform the many pieces of data into actionable knowledge. Quantitative science methodologies are indispensable for advancing the understanding of cancer etiology, prevention, and therapeutic intervention.
Division members apply state of the art methodologies and, when appropriate, invent new methodologies to advance data-driven, team-science. The Division is committed to the highest standard of scientific practice and ethics, supporting principled design of clinical trials and experiments, rigorous execution, transparent reporting, reproducible research, and software development.
Dissemination of quantitative science resources throughout the SKCCC are supported through the three shared resources housed in the Division: Biostatistics, The Experimental and Computational Genomics Core (ECGC), and Research Information Technology Systems (RITS). Collaboration amongst members of the Division and other quantitative scientists from Departments across Johns Hopkins University Schools enables the rapid embedding of cutting edge methodological advances into cancer research. Collaborations with University-wide initiatives such as Precision Medicine, Artificial Intelligence, and Convergence Science promote dissemination for quantitative biomedical research. The Division also coordinates various educational activities to empower the next generation of cancer biologists and clinical investigators in quantitative science methodologies and study design. Through these efforts, the Quantitative Sciences Division leads state of the art computing for cancer research and pioneers computational pipelines applicable to biomedical research at large.