Cancer Data Science Course
Are you interested in the field of cancer data science? Whether you’re intending to transform your career with it, or you’re just looking for some perspective, we hope this beginner series can be your roadmap to better understanding this pivotal field.
Watch, read, and review your way through each chapter to learn fundamental information and skills.
Chapter 5: Machine Learning for Cancer Research
Why should you use artificial intelligence/machine learning (AI/ML) in your cancer research project? By the end of this chapter, you’ll break down this buzzword to better understand what AI and ML techniques are, get tips for where to start, and see how NCI-funded researchers leverage the technology for their projects.
Start the Course
Watch our ~6-minute-long video, “5 Tips for Using Machine Learning in Your Research”
Test your knowledge!
Other Related Materials:
- NIH-supported Scientific Data Repositories: Browse the list of imaging, genomic, proteomic, animal, pre-clinical, and clinical data sets. Details also include institutes/centers affiliated with the repositories, and whether or not open data may be submitted.
- AI for Efficient Programming: Develop your understanding of how you can use large language models to do common programming tasks faster. In this course, there are examples and hands-on activities for you to practice and test your knowledge.
- AIM-AHEAD Research Fellowship: Visit the website to learn more about this program and when/how to apply.
- National Library of Medicine: Peruse the largest biomedical data library in the world.
- NCI-DOE Collaboration Machine Learning Resources: Explore the open access software, models, and data developed through NCI’s collaboration with the Department of Energy.
- Interpretable and Explainable Deep Learning: Watch this presentation on deep neural networks, including challenges, frameworks for evaluation, and how to apply that framework across different data sets.
- Did the Machine Get it Right? Learning to Trust Neural Networks in Medical Applications: Find out what the “Black Box” in machine learning is in this short blog about applying neural networks to medical applications.
- Theranostics and AI—The Next Advance in Cancer Precision Medicine: Learn how AI is supporting research in chemotherapy, radiation, and other therapeutic strategies to diagnose and treat cancer.
- Next Generation Artificial Intelligence: New Models Help Unleash the Power of AI: Learn about the benefits and challenges of using foundation models (unsupervised machine learning models) in cancer research.
- Blending Weather Forecasting with Team Science Leads to Advances in Cancer Immunotherapy: Read another use case on AI being blended with spatial and single cell to technologies to understand how cancer will respond to treatment.
- Machine Learning Tool Offers Insight Into Cancer Treatment for Older Patients: Learn how this tool can be used to predict which patients are more likely to benefit from a treatment.
- NCI-funded Machine Learning Gives New Insight into Endometrial Cancer: Discover how this machine learning model offers a framework that may someday allow clinicians to quickly and efficiently target medications to specific genetic mutations without the need for in-depth and costly genetic sequencing.
- Deep Learning Tool Predicts Risk for Pancreatic Cancer: Read how NCI-funded researchers collaborated with Denmark scientists to develop a deep learning tool for predicting risk for pancreatic cancer.
- NCI-Supported Study Uses Deep Learning for Cancer Prognosis: Discover a model that gives researchers the ability to examine pathology whole slide images and molecular profile data from 14 cancer types.
- Machine Learning Modeling Generates Accurate Insights into RAS-membrane Biology: Read the journal article that details how a multiscale platform called “MuMMI” is using machine learning to investigate RAS activation events.
- New Deep Learning Tool Accurately Classifies Tumor Types: Read the journal article on how NCI and Department of Energy researchers developed a tool called “TULIP” for classifying human RNA-sequence-based tumor types.
- Ready to start your project? Get an overview of the data science lifecycle and what you should do in each stage.
- Need answers to
data sciencequestions? Visit our Training Guide Library.