As a National Cancer Institute-designated cancer center, the UF Health Cancer Center is accelerating cancer research by leveraging how data is harnessed, organized and analyzed across disciplines.
On April 29, data experts in fields from biostatistics to biomedical engineering to health informatics to surgery gathered for the inaugural Cancer Computational Biology Mini-Symposium at the Cancer and Genetics Research Complex. The event, presented by the center’s Biostatistics and Computational Biology Shared Resource, featured rapid-fire research presentations by nearly a dozen University of Florida experts working in this innovative space.
Researchers discussed a variety of topics such as the discovery of new genes that increase cancer risk by taking advantage of improved analytical and statistical power to uncovering the role of ancestry in cancer with large-scale genomics data. Using leading-edge data approaches such as machine learning, researchers are advancing personalized cancer treatment, predicting drug targets and unraveling how tumors uniquely mutate.
“Our mission is to advance cancer research across the University of Florida by ensuring high-quality data analysis is applied to all studies, whether that’s large language models or small case studies,” said Ji-Hyun Lee, DrPH, associate director for cancer quantitative sciences at the Cancer Center.
Attendees from UF colleges ranging from Pharmacy to Veterinary Medicine had a chance to learn about the latest digital innovations, interact with experts and network with colleagues. “To prevent researchers from working in silos, we want to provide opportunities for investigators to connect in collegial settings and foster collaborations that advance science,” Lee said.
The event also provided an opportunity for the new co-leaders of the Cancer AI Working Group to introduce their research. The expanded leadership is part of the center’s broader enhancement of data infrastructure to propel cancer research to new heights.
The new leaders have complementary expertise in data science. Ali Zarrinpar, M.D., Ph.D., is a physician-scientist and professor in the division of transplantation and hepatobiliary surgery whose focus is finding quantitative measurements of health and using them to optimize patient care. Muxuan Liang, Ph.D., is an assistant professor in the department of biostatistics whose goal is to realize data-driven health care decision-making through innovative methods combining statistical and machine learning techniques. Qianqian Song, Ph.D., is an assistant professor in the department of health outcomes and biomedical informatics whose research is advancing precision medicine and personalized therapy through a data-driven informatics approach and integration of multimodality biomedical data.
“The deliberate partnerships between physicians and data scientists in this multidisciplinary group really helps to ensure questions are clinically relevant and answers can be rapidly translated in support of improved patient outcomes,” said Thomas George, M.D., deputy director of the UF Health Cancer Center. “The Cancer AI Working Group is a critical avenue for the Cancer Center to accelerate scientific innovation across each of our four research programs, leveraging the university-wide investments in informatics and digital analytics to the benefit of our patients.”
The Cancer AI Working Group, organized in partnership with the Biostatistics and Computational Biology Shared Resource and the Cancer Informatics Shared Resource, was launched in 2021 to facilitate collaborative research, expertise and network capacity in AI. Its mission is to capitalize on and grow AI capacity at UF to advance AI applications in basic and translational cancer research, bridge interdisciplinary expertise and foster early-career investigators. Zarrinpar, Liang and Song take the helm from outgoing leaders Mattia Prosperi, Ph.D., FAMIA, and Qing Lu, Ph.D.
At the symposium, the 2023 recipients of the Cancer Center’s AI pilot grants, Rui Yin, Ph.D., and Xiangyang “George” Lou, Ph.D., also presented updates on their work. Lou shared advancements in using AI to improve quantification and resolution of magnetic particle imaging, an innovative, noninvasive tomographic modality that has implications for cancer detection. Yin discussed his work using AI-driven precision medicine to improve public health outcomes and health equity, such as using convolutional neural networks to predict high-confidence mRNA-miRNA pairs in patients with colorectal cancer.
The symposium is anticipated to become an annual event moving forward.
Discover the Cancer AI Working Group
The mission of the Cancer AI Working Group is to capitalize on and grow AI capacity at UF to advance AI applications in basic and translational cancer research, bridge interdisciplinary expertise and foster early-career investigators.
