Join this upcoming webinar hosted by the National Institute of General Medical Sciences (NIGMS). Presenters will discuss a cloud-based learning platform that contains modules you might use in your cancer research!

Interested in learning about a large, patient-centered, cancer health outcomes data resource? Join Drs. Jensen and Lines as they discuss the navigation, extraction, analysis, and interpretation of Medicare Part D claims data with cancer outcome data from the Surveillance, Epidemiology, and End Results-Medicare Health Outcomes Survey (SEER-MHOS) resource.

Are you attending this year’s Annual Congress of the European Association for Cancer Research in Torino, Italy? Listen to NCI CBIIT’s Dr. Daoud Meerzaman discuss how multimodal artificial intelligence and machine learning approaches can assist in predicting outcomes for cancer patients.

Join this year’s HTAN Summer Meeting on June 12–13 to learn more about the HTAN resources. See how these single-cell resolution atlases, which capture changes in tumors over space and time, can help advance your cancer research.

Dr. Sobreira will discuss PhenoDB, a web-based tool developed for the collection, storage, and analysis of phenotype data.

Join NCI Chief Information Officer Mr. Jeff Shilling as he discusses how cloud-based open science models are benefiting the Cancer Moonshot’s mission to end cancer as we know it.

During the June NCI SeqSPACE webinar, Drs. Haeussler and Cline from the University of California Santa Cruz (UCSC) Genomics Institute will discuss their respective projects: UCSC Genome Browser and BRCA Exchange. They’ll also highlight data resources for clinical variant interpretation.

Want to know more about a cancer research project studying osteosarcoma and leiomyosarcoma through patient data sets? Register for May’s Cancer Moonshot seminar!

Dr. Tiwari will share her lab’s latest research in developing machine learning techniques to understand tumor biology as studied through non-invasive imaging, histopathology, and omics data.

Sensitive data removal is critical to medical image sharing. Attend this two-day workshop to learn more about this topic and the role data science plays in de-identifying the data.