Skip to main content
The NCI Community Hub will be retiring in May 2024. For more information please visit the NCIHub Retirement Page:

NEW NCI Cancer Research Data Commons (CRDC) Request for Information (RFI) on AI Readiness! Responses due: November 30, 2023.  NCI's CRDC seeks broad community input on the artificial intelligence readiness (i.e., data representation in a format that enables and eases the application of artificial intelligence/machine learning (AI/ML) approaches) of data across the multiple components of NCI’s Cancer Research Data Commons (CRDC). For more information, visit htt…



Calling All Cancer Research Innovators! Provide your feedback to NCI’s Cancer Research Data Commons (CRDC) Request for Information (RFI) on AI/ML use cases by November 30! View the full RFI at this link: Don’t miss this opportunity to impact healthcare innovation!


Image result for images for cancer analyticsThe Second ECICC Community MicroLab on Cancer Challenges and Advanced Computing was held on September 25, 2019!

  • What is a MicroLab? MicroLabs are 60-90 minute, highly interactive, virtual events. Unlike webinars which are focused on disseminating information, the purpose of MicroLabs is to facilitate stimulating scientific discussions in smaller more intimate virtual breakout groups.

  • A multi-disciplinary group of over 100 clinicians, researchers, and academics in cancer and computational sciences participated in our second virtual, ECICC Community MicroLab on September 25, 2019!

  • Building on the breakout discussions from the first MicroLab held in June 2019, participants developed use cases for real-life situations and then identified what research challenges need to be overcome. The use cases were based on various personae derived from the 4 cancer challenge areas developed at the Envisioning Computational Innovations for Cancer Challenges (ECICC) Scoping Meeting held in March 2019.


MicroLab Presentations:


Presenters Included (partial list):

  • MicroLab Origins

    • Emily Greenspan, National Cancer Institute

  • Generating Large-Scale Synthetic Data to protect Personally Identifiable Information

    • Nick Anderson, University of California, Davis

    • Bill Richards, Brigham And Women's Hospital / Harvard University

  • Using Machine Learning for Iterative Hypothesis Generation

    • Amber Simpson, Queen’s University 

  • Creating a Cancer Patient “Digital Twin” to optimize personalized treatment decision-making

    • Tina Hernandez-Boussard, Stanford University

    • Paul Macklin, Indiana University

  • Developing Adaptive Cancer Treatments targeting unique tumor characteristics and trajectories

    • John McPherson, University of California, Davis

  • Use Case Demonstration

    • Paul Macklin, Indiana University


If you are interested in learning more -- or joining -- this multi-disciplinary community, please contact 


Created by Malachi Greaves Last Modified Mon April 25, 2022 4:00 pm by Lynn Borkon