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Publications Highlight the Power of Deep Learning for Cancer Drug Response Prediction

In the latest publications from the NCI and Department of Energy (DOE) Collaboration, you’ll learn about the impact of deep learning on predicting drug response in cancer. Teams from NCI’s Frederick National Laboratory for Cancer Research and DOE’s Argonne National Laboratory published the works.

One article focuses on trends in deep learning methods for drug response prediction in cancer. The other is about data augmentation and multimodal learning for predicting drug response.

The NCI-DOE Collaboration fosters collaborative biomedical research by providing forward-looking computational models, algorithms, data sets, software, and other resources.

Dr. Ryan Weil, co-lead of the NCI-DOE IMPROVE project, says, “These papers show how the collaboration is doing team science to drive the application of deep learning in cancer drug response prediction. The trends in deep learning article sets the stage for understanding the field of artificial intelligence-based response prediction models, which is an extremely active field. The paper about multimodal learning highlights the NCI-DOE efforts to explore the use of richer, less structured data like images to improve predictive power.”

These resources are openly available to the research community. Find more NCI-DOE publications on our NCI-DOE Collaboration Publications page.

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