Johns Hopkins University’s Dr. Ji will present, “Identifying and Preventing Artifacts in High Dimensional Data: Computational Science in Immuno-Oncology.” This is the third of nine seminars in the SITC-NCI Computational Immuno-Oncology Webinar Series, a series designed for data scientists interested in the latest data science technologies and immunotherapy research.

During a session at the two-day CCR-DCEG Health Disparity Workshop, CBIIT’s Dr. Jill Barnholtz-Sloan will be presenting, “Data resources for data science to advance cancer health disparity research” with moderation by Sheila Rajagopal.

Dr. Peng Jiang of NCI’s Center for Cancer Research will discuss CytoSig, a software-based platform that provides both a database of target genes modulated by cytokines and a predictive model of cytokine signaling cascades from transcriptomic profiles. NCI is seeking parties interested in licensing and/or co-developing the platform.

During this session of the Big Data Training for Cancer Research Special Lecture Series, Duke University’s Dr. Warren Kibbe will present on the NCI CCDI. Learn about the initiative’s current state, future direction, and how to get involved.

At the AWS Summit, NCI CBIIT’s Dr. Keyvan Farahani will take part in a panel discussion, titled “Elevating a Patient’s Radiology Journey with ML and the Cloud.” The panel will examine how medical imaging techniques, including radiology and pathology, are being blended with machine learning and cloud technologies to improve patient care.

During this session of Big Data Training for Cancer Research Special Lecture Series, Dr. Jill Barnholtz-Sloan will present, “#DataMatters—Leveraging Big Data for Impact on Cancer.” Learn about the ubiquity of data and its influence on cancer research based on specific examples in brain tumors.

Discover how computing and machine learning can accelerate molecular optimization for cancer and infectious disease therapeutics during the May ATOM Webinar Series.

NCI is conducting a two-day virtual workshop, Machine Learning (ML) and Health Outcomes in Cancer Care Delivery Research, highlighting current cancer-focused research in health outcome risk prediction, symptom trajectories, natural language processing methods, and point-of-care decision support. Panel sessions will identify the strengths and potential of ML methods, barriers, and opportunities specific to cancer care delivery research.

Register for the May Containers and Workflows Interest Group webinar to learn how investigators are leveraging Amazon Web Services capabilities for genomics research.

Learn how to overcome common challenges using multi-institutional data during this NIH Data Sharing and Reuse Seminar. Dr. Kalpathy-Cramer will review examples of privacy preserving learning from multi-institutional data sets and discuss successes as well as directions for future research.