12. NCI’s National Cancer Plan and Data’s Critical Role

In May 2023, NCI CBIIT’s Leadership Team gave their insight into the new National Cancer Plan, which is NCI’s vision for the future of cancer research. Discover what leadership had to say, in particular, and how data science and technologies play a critical role in supporting future cancer research at NCI.

11. Federated Learning—A Solution for Democratizing Data for Cancer Research?

Blog #11 asks the important question: What can federated learning (FL) do for your cancer research? This decentralized approach to accessing, analyzing, and interpreting data has many advantages. For instance, learn how FL enables you to conduct analytical modeling without moving any data from their place of origin!

10. How the Mitelman Database Can Help You Explore Genomic Abnormalities

Are you researching genomic abnormalities? In our tenth most-read blog of the year, Bioinformatician Deena Bleich gives an overview of the online tool, “Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer,” and how it can help you analyze genomic data.

9. Semantics Series—A Deep Dive Into Common Data Elements (CDEs)

In blog #9, discover how you can use CDEs to assign meaning to data in a way that’s predictable, consistent, and persistent. CBIIT’s Ms. Denise Warzel and Dr. Gilberto Fragoso take a deep dive into CDEs and show how they help you define, map, and use data more efficiently.

8. NCI Funding Helps AI Researchers Gain Global Insight on Cancer 

The #8 slot goes to Drs. Ajay Aggarwal and Anant Madabhushi, two grantees funded by NCI’s Center for Global Health’s Affordable Cancer Technologies (ACTs) Program. Learn how these two are using artificial intelligence to develop tools for a variety of conditions and health systems around the world.

7. Career Confessions: 5 Things a Research Fellow Learned in a Cancer Data Scientist Lab

Are you pursuing a career in data science? We spoke to an early-career cancer data scientist at NCI to get advice that can help you! Hear the five things he’s discovered since receiving his doctorate, plus the experience he’s gained in the field.

6. Visualizing Data Using Circular Heatmaps and Biplots—Pro-Tips From NCI Researchers

Explore circular heatmaps and biplots with Drs. Arashdeep Singh and Sridhar Hannehalli of NCI’s Center for Cancer Research! In this edition of CBIIT’s series on data visualizations, we dive into a popular method for visualizing high-dimensional data consisting of two or more variables.

5. Using Cancer Data Science to Advance Your Research

Coming in at #5, several people interested in cancer research wanted to see how data science can help them in their work. Read our simple guide that will show you how data science can improve your cancer research.

4. Trusting the Data—A Look at Data Bias

Blog #4 asks the big question: Can we truly trust the data after studies uncover (often unintentionally) bias? Read Dr. Vivian Ota Wang’s perspectives on data bias and her ideas for making data more equitable, fair, and useful to the greatest number of people.

3. Job Interview Questions for Cancer Data Scientists and How to Answer Them

If you’re considering a new job in the new year, blog #3 is for you. See what one NCI hiring manager—CBIIT’s Dr. Daoud Meerzaman—is looking for when interviewing a candidate for a cancer data scientist job. Dr. Meerzaman provides his input on what certain questions mean during the interview process.

2. NCI Brings Cancer Data to Life

Have you ever been curious about what goes into making data ready for analysis by researchers worldwide? Watch (it’s our one-and-only video blog!) and be amazed at how “Datum,” a tiny speck of genomic data, grows to larger-than-life proportions thanks to cancer researchers at NCI.

1. Theranostics and AI—The Next Advance in Cancer Precision Medicine

Our top blog of 2023 taught our readers, “if you can see it, you can treat it.” Dr. Baris Turkbey, senior clinician in NCI’s Molecular Imaging Branch at the Center for Cancer Research, explores the field of theranostics. He describes how artificial intelligence and data are helping researchers “see” cancer in a new way, resulting in a more precise way of targeting cancer treatment.