Cancer Data Science Pulse

High-Performance Computing (HPC)
Illustration of a clear-glass capsule that houses multiple balls that depict icons referencing data types. This includes icons of statistics, childhood cancer, cloud, and partnerships.

NCI’s Data Science Time Capsule—A Snapshot in Time

Watch our time capsule video to learn about the current status of the field and new technologies that are sure to be important as we embark on the next era of cancer data research.

Illustration of a digitally rendered cell.

Meteorology, Aerospace Engineering, and Cancer Research—The Future of Predictive Modeling

What do winter storms, airplanes, and cancer research have in common? In this blog, experts on meteorology, aerospace engineering, and radiation oncology explore what we can learn from these very different fields to further advance how we target and apply radiation to more effectively treat cancerous tumors.

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Predictive Modeling for Pre-clinical Drug Screening: Improving Models Derived From Observational Studies Using Machine Learning and Simulation

This blog post, the fifth, concludes our series that discusses the principles underlying the collaborative project "Joint Design of Advanced Computing Solutions for Cancer (JDACS4C)."

Population Level Pilot: Population Information Integration, Analysis, and Modeling for Precision Surveillance

NCI continues to identify and link external data sources with SEER data to enable the expansion of longitudinal data to form patient trajectories and to support modeling efforts. To inform the incorporation of those additional sources, NCI compiled an extensive breast cancer recurrence data dictionary to identify recurrence-related data elements across multiple sources, including pathology, radiology, pharmacy, biomarkers, procedures, comorbidities, patient-generated information, and radiation oncology.

Surface representation of the structure of oncogenic mutant of KRAS (colored violet) in complex with GTPase-activating proteins (colored light blue).

Modeling the Dynamics of Membrane-bound Mutant RAS to Accelerate Discovery of Novel Drug Targets

This is the third in a series of posts that discuss the principles underlying the three-year collaborative program "Joint Design of Advanced Computing Solutions for Cancer (JDACS4C)."

Using CANDLE deep learning to extract protein folding intermediate states.

CANDLE: Scaling JDACS4C Machine Learning Algorithms to Unprecedented Magnitudes

This is the second of a series of posts that discuss the principles underlying the NCI-DOE Collaborations.

Graphic depicton of JDACS4C Pilots and CANDLE

Joint Design of Advanced Computing Solutions for Cancer (JDACS4C): The Right Collaboration at the Right Time to Accelerate Cancer Research

This is the first of a series of posts that discuss the pilot collaborative program “Joint Design of Advanced Computing Solutions for Cancer (JDACS4C)” being pursued by the National Cancer Institute (NCI) and the Department of Energy (DOE).

Decorative image of cartoon-style drawings of buildings with a cloud above them, rays of light coming from the cloud towards the buildings.

Democratizing Access to CCG Data: Cancer Genomics Cloud Pilots

In recent years, genomics has been described as a big data science on par with the likes of Twitter, YouTube, and the scientific pursuit of understanding the universe.

Venn diagram with three overlapping circles. Circle 1: Predictive Modeling & Simulation (Molecular Dynamics, Computational Chemistry). Circle 2: Analysis, Search & Compare (Data Science, Analytics). Circle 3: Data Transformation & Management (Genomics, Image Processing).

Precision Medicine Inspires HPC

The recent weeks have been momentous as the high-performance computing (HPC) community embraced the challenge of precision medicine.

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