Cancer Data Science Course

Watch our video, “5 Tips for Using Machine Learning in Your Research” (approx. 6 minutes long).

• 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.
• AI for Decision Makers: With this course, you’ll learn how to make strategic decisions, drive innovation, increase efficiency, and promote a culture that embraces the power of AI technologies.

Keep Going!

Continue to Chapter 6 where you will learn about the unique professionals on the multidisciplinary cancer data science team (and where you fit in).