Cancer Data Science Pulse

Using Cancer Data Science to Advance Your Research

Data science can help advance your work in cancer research and medicine. No matter where you are in your data science journey, we have articles and resources for you to explore.

Data science captures various techniques and tools that researchers, clinicians, and data scientists use to drive the field of cancer research forward. Maybe machine learning can help you predict patient responses and treatment plans, or data visualization methods will help you more effectively share your findings. Perhaps you’re curious about what a cancer researcher needs to know about working with a data scientist. Get answers to these questions, and more, from the following articles!

Data, Clinical Trials, and You

Performing a CIViC Duty—A Community-Driven Resource for Interpreting Data on Cancer Variants

If you’re planning to set up a clinical trial, you can use the information in this blog to help you determine your baselines and parameters. See what’s been done with different therapeutics and the outcomes before you set up your trial! You’ll also learn about CIViC, an open-source platform that connects researchers to the latest published findings on a wide range of cancer variant interpretations. 

Making Sense of Data Sharing (and Management)

Your Guide to the 2023 NIH Data Management and Sharing Policy

Whether you’re new to data science or have questions about properly managing and sharing the data you work with, this blog can help. Effectively sharing data with others in the cancer community greatly impacts the field’s advancement; everyone benefits from following this policy!

Funding for Data Science Projects

Discover the New Index of NCI Studies Web Application to Get NCI Research Outputs 

If you’re looking for examples of how NCI funding could help support data science in your work, check out this article on the Index of NCI Studies. You’ll be able to browse through the outputs from NCI-supported grants and get a better sense of NCI funding and data for cancer research.

NCI-Funded Partnership Uses AI to Improve Diversity in Cancer Clinical Trials

Are you passionate about improving diversity in clinical trials? Data science plays a vital role in ensuring clinical trials are representative. This article will give you a look at how NCI-funded researchers partner with experts to improve diversity in cancer research through artificial intelligence.

You’ve Got Data! Now What?

Visualizing Data Using Circular Heatmaps and Biplots—Pro-Tips from NCI Researchers

Are you comfortable with the basics of data science but need help using your data most effectively? As the name suggests, data visualization lets you create a visual component to your data, which can be helpful in gaining insights from your data and sharing your data with others. In this blog, you’ll learn more about two types of data visualization graphics that you may want to use in your research.

Collaboration is Key

Federated Learning – A Solution for Democratizing Data for Cancer Research?

Does the scope of data science seem overwhelming? There are vast amounts of data from many sources, challenges with data diversity and bias, and accessibility issues to consider. This blog walks you through a novel approach to using data science for cancer research. Give this a read if you’re curious about the impact of collaboration across the field of cancer data science.

Now that you’ve had a chance to explore data science topics and think you might be interested in learning more about the tools available to you; where do you go? Check out the sampling below of some of the types of tools and resources you can access!


Resource/Tool Name

What is it?

What does it do?

How can you use it?

ATOM Modeling PipeLine


The ATOM Modeling PipeLine (AMPL) is an open source, modular, extensive software pipeline for building and sharing models.

Use AMPL to generate machine learning models for your work in advancing silico drug discovery.



CANcer Distributed Learning Environment (CANDLE) is an open source software platform that improves prediction accuracy of machine learning algorithms.

Explore CANDLE to find resources that help with hyperparameter optimization for more accurate machine learning in your cancer research.



Informatics Technology for Cancer Research (ITCR) is an NCI-program supporting informatics technology across cancer research.

Visit this website to discover the wide range of tools in the ITCR portfolio. You can use these tools for your work in omics, imaging, network biology, clinical research, and data standards.

Cancer Drug Response Prediction

Data Set

This provides dataframes (e.g., gene expression and drug response data, etc.) and supporting metadata used in the Pilot 1 project.

Check out this resource if you’re looking into systematically modeling tumor drug response with deep learning models more suited for large-scale data. 

Are there additional topics you’d like to see covered to help you understand what data science can do for you and your research? Is there something you wish an informatics tool could make simpler for you? Leave a comment below and we’ll follow up with more information!

You can also subscribe for a weekly email highlighting new resources, events, and opportunities.

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Curing cancer may result in a demographic catastrophe
Thank you for your interest in the topic. NCI's mission is to lead, conduct, and support cancer research across the nation to advance scientific knowledge and help all people live longer, healthier lives. At CBIIT, we're committed to using data science to advance that goal!
The visuals you've included in this post really help to illustrate your points. They add a lot of value and make the content more engaging.
Thank you for the feedback; we’re glad to hear you found the visuals engaging! We’ll keep that in mind with our future pieces as well. We hope you’ll come back to read more!
Hey there, Cancer Data Science team! I just finished reading your article on using cancer data science to advance research, and I must say, it's truly fascinating how data-driven approaches are revolutionizing the field of oncology. As someone who has a personal connection to cancer, I appreciate the tireless efforts you and your team put into finding innovative ways to combat this disease.

Your article did an excellent job of highlighting the potential of data science in uncovering patterns, identifying risk factors, and developing targeted treatments. It's incredible to think about the vast amount of data available, and the possibilities it holds for gaining deeper insights into the complexities of cancer. The examples you provided, such as using machine learning algorithms to predict patient outcomes and mining electronic health records for valuable information, showcased the power of these techniques in improving patient care and driving scientific discoveries.

I also found it inspiring how you emphasized collaboration and the importance of open data sharing within the scientific community. By fostering a culture of cooperation, you enable researchers from different disciplines to pool their expertise and resources, ultimately accelerating the progress in cancer research. It's truly heartening to see how data science is breaking down traditional silos and paving the way for a more collaborative and impactful approach to tackling cancer.

In conclusion, I want to express my gratitude to you, the Cancer Data Science team, for your dedication and innovation in the fight against cancer. Your work is making a real difference in the lives of patients and their families, and I'm confident that the future holds even greater possibilities as data science continues to advance. Keep up the fantastic work, and I'll be eagerly following your updates and discoveries!

Warm regards,
[PMO Global Institute]
We’re glad you’re so enthusiastic about the potential of data science in cancer research; so are we! Thank you for taking the time to read our blog and leave a comment. If you haven’t already, please consider subscribing to our weekly email updates by clicking the Subscribe button on this page.
The article has a beneficiary content, which has helped me a lot in understanding ……… It is a very informative blog which everyone should read to know.
Thank you for your interest in our blog. We’re glad you’ve found it beneficial!
Its a such a great article. Thanks for providing wonderful information
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