Cancer Data Science Pulse

NCI’s Data Science Time Capsule—A Snapshot in Time

This video captures the current status of the data science field and describes some new technologies that are sure to be important as we embark on the next era of cancer research.

Find Out Whats in the Time Capsule

Leave us a comment below to let us know if the time capsule is missing anything. Is there a theme or word/phrase you most associate with cancer data science? Submit your thoughts by midnight on December 30, 2022. Well consider the comments for a final entry before the time capsule is officially buried.

NCI CBIIT Staff
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I'd add "digital twins" to this. I find it fascinating that the field is building on NASA's efforts to develop a computational and mathematical representation of a patient. Having watched many family and friends, I've learned that fighting cancer is also living with it. The comorbidities, that may happen as an effect of living with the disease or the course of treatment, can have devastating, life-long impacts. It’s inspiring to think that a computer could both simulate those potential risks and “experience” the pain in place of my loved ones and help their physicians better plan a course of treatment. To me, it means that they could live a better quality of life despite cancer. I’m really excited to see where the cancer data science field goes in developing and guiding more precision medicine approaches like this!
Thank you for suggesting digital twins! Enhancing patient outcomes and patient quality of life over the course of cancer treatment is an important role of data science. You might be interested in reading a news article we have on the site about digital twins and their role in cancer care: https://datascience.cancer.gov/news-events/news/commentary-describes-how-digital-twins-could-transform-cancer-care
I think one thing to add would be the specific achievements of cancer systems biology. For example, the progress in using mechanistic, bottom-up molecular-pathway models to understand and design combination therapies. Or, the data-driven, top-down systems biology findings improving our understanding of cancer-relevant molecular networks and cancer drug targets.
We appreciate you taking the time to contribute to the time capsule. Thank you! The intersection of data science and systems biology for cancer research is a great addition. The ability to use data, models, and other resources to improve our understanding of cancer and cancer treatment continues to advance the field.
Great collection for the time capsule! I'd like to add data sharing, FAIR principles, data analytics, biostatistics, and bioinformatics.
These words and phrases make sense for 2022 and beyond. Thank you for submitting them! The Cancer Data Science Pulse Blog has posts highlighting these very topics as well. https://datascience.cancer.gov/news-events/blog
Research
Thank you for submitting the word ‘research,’ and for your interest in the time capsule. The included themes, and those submitted from the larger data science community, are all helping advance the future of the field.
Interesting! I would add the word "power."
Thank you for your submission. Let your colleagues know we are interested in hearing from them as well! We have other blog articles on the theme of ‘power’ in cancer data science, that support your thought! https://datascience.cancer.gov/search/node?keys=power+blog
Thank you for opening this up to submissions. It’s an interesting collection and I’m excited to be able to contribute. 10 or 20 years from now, will we still be saying that we need more data for data science projects or will we have generated enough data and new approaches to overcome any remaining data limitations. I would also add two programs that include significant data science elements, the NCI Human Tumor Atlas Network and the NIH 4D Nucleome Program, to the time capsule.
The NCI Human Tumor Atlas Network and the NIH 4D Nucleome Program are both excellent programs that advance data science and cancer research; thank you for submitting. Be sure to let your colleagues know that we’d like to hear their thoughts on the future of data science as well!