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Have You Considered Using Data Science for Your Cancer Research?

If you’re on this page, you’re wondering how you can leverage the power of data science for your cancer research (or your principal investigator has asked you to figure it out). Regardless, we at NCI want to support your data science learning journey, because we know the next generation of cancer researchers have big data problems to overcome. 

Whether you want to become a cancer data scientist or you’re just trying to figure out what you need to learn, we created this resource to answer:

Why Apply Data Science to Cancer Research? 

Cancer research generates a lot of data that is very difficult to analyze. However, this large-scale research is critical to making possible precision medicine efforts, or treatment options tailored to the personal genome of a patient. Cancer data science isn’t one field, but the intersection of many, using cutting edge technology to work with, analyze, visualize, and learn from ever-increasing amounts of data.

Why is Data Science Valuable to NCI?

As the nation’s leader in cancer research, NCI develops the technology, tools, and techniques that you and our own cancer researchers and data scientists can use. Identified as one of the eight goals outlined in the National Cancer Plan, maximizing data utility through data science will accelerate the speed and extent to which we understand and treat cancer.

In your future projects, you might work with NCI data science initiatives. Here are just a few examples:

• You could share your research data or access other data sets in NCI’s cloud-based repository, the Cancer Research Data Commons.
• You may try out artificial intelligence (AI) and machine learning (ML) algorithms to investigate clinical targets through MoDaC.
• You might explore the pediatric molecular targets platform to study why some childhood, adolescent, and young adult cancer patients respond differently to treatments.

Why is Data Science Valuable to You?

We’ve heard early career investigators ask: 

• “Why do I need to learn this?”
• “Why learn this when I work with a data scientist?”
• “Can’t I just Google it or use ChatGPT?”

Late-career researchers told us they wished they had learned these basic skills when they started.

Basic data science skills can help you:

• expand your repertoire of research questions by blending different kinds of data (i.e., genomics, imaging, pathology, clinical studies) for integrative research. 
• create algorithms that clean your data faster.
• improve your data visualizations for your publications. 
• understand and evaluate the use and limitations of AI, ML, and large language models. If you don’t know how AI works, how can you have confidence in the results?
• be more marketable in your career. 

Now that you’ve seen the possibility of cancer data science, how do we do it at NCI?