Hear about NCI-supported work with the artificial intelligence (AI) platform, “Molecular Twin,” and how researchers are using it to predict disease survival. This webinar is part of the NCI Rising Scholars Cancer Research Seminar Series.

Attend this Data Science Seminar to learn how artificial intelligence (AI) will revolutionize healthcare by enhancing diagnostic precision, optimizing treatment plans, and improving patient outcomes.

Hear from NCI CBIIT’s acting director on how you can find and leverage cancer data to accelerate the development of cancer research.

Learn more about RadxTools, an image informatics toolkit that uses artificial intelligence (AI) to help clinicians with precision medicine approaches. NCI’s Informatics Technology for Cancer Research Program funds this toolkit.

Learn about advanced computational methods for analyzing cancer-related genetic data. This event is part of NCI’s Emerging Technologies Seminar Series, highlighting NCI-supported novel technologies that could transform cancer research and clinical care.

Hear NCI CBIIT’s Dr. Jill Barnholtz-Sloan describe how NCI data science resources can help you with your cancer research!

Learn more about spatialGE—a web application and R package that can provide you with a platform for spatial transcriptomics data analysis and visualization for your cancer research. NCI’s Informatics Technology for Cancer Research program funds this application.

Register for this webinar to learn more about UCSC Xena, a tool that gives you an integrated, secure, and user-friendly way to access genomics data. Through this presentation, you will learn how to use visualizations to view your own data in a secure environment.

Register for this Data Science Seminar to hear how artificial intelligence (AI) is impacting cancer research and clinical diagnosis by transforming the field of pathology, particularly by enhancing the assessment of tissue samples and microscopic images.

Join NCI’s Division of Cancer Control and Population Sciences for a discussion about software tools that study particular types of images and output data that are suited for influencing cancer prevention and control.