Cancer Data Science Pulse

Machine Learning
Illustration features two histology slides and shows how a human interprets the images compared with a computer.

Machine Learning and Computer Vision Offer a New Way of Looking at Cancer

Artificial Intelligence offers boundless possibilities, especially in the healthcare field. In a recent CBIIT Data Science Seminar, Dr. James Zou showed how Computer Vision (CV) is helping create a new data-driven “language of morphology” that allows researchers to be more precise in interpreting histological images. Just as computers help propel self-driving cars along busy roadways, CV offers a faster, less-subjective method for assessing disease.

Illustrated drawing of a large group of diverse people. Headline: BIG DATA Meets Social Determinants of Health-thumbnail-MDT.png

How Can Big Data Help to Address Health Disparities?

Dr. Tony Kerlavage, director of NCI’s Center for Biomedical Informatics and Information Technology (CBIIT), sat down to discuss one key component of racial inequality, the issue of health disparities, as it relates to Big Data. As noted by Dr. Kerlavage, representing our diverse U.S. population in research and in the workforce are key, but we also need better data.

Lee Cooper, Ph.D.

The Promise and the Challenge of Deep Learning in Pathology

One of the most exciting developments of the past decade has been the success of methods broadly described as deep learning. While the roots of deep learning date back to early machine learning research of the 1950s, recent improvements in specialized computing hardware and the availability of labeled data have led to significant advances and have shattered performance benchmarks in tasks like image classification and language processing.

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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 three-year collaborative program “Joint Design of Advanced Computing Solutions for Cancer (JDACS4C).”

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