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The NIH ScHARe social science data repository and research collaboration platform has been launched. This innovative ScHARe platform will:

  • host a wealth of social determinants of health and other social science datasets
  • provide cloud computing tools and secure workspaces to allow easy, low-cost analysis of these data
  • foster collaborations to develop AI bias mitigation strategies and advance health disparity and health care delivery research using big data

The ScHARe platform also…

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The National Cancer Institute (NCI) is hosting a virtual workshop focused on methods to address data limitations for applying artificial intelligence approaches in cancer research. The Cancer AI Research: Computational Approaches Addressing Imperfect Data workshop will run April 3-4, 2023, from 11 AM - 5 PM ET each day.

The goals of this workshop are to (1) examine the state of the science for AI methods designed to operate on noisy, complex, or low-dimensional data; (2) explore…

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Submit Your Manuscript for a special issue of Cancers: “Modeling Strategies for Drug Response Prediction in Cancer.”

Increase the visibility of your research in deep learning-based drug response prediction by submitting a manuscript to this special issue of Cancers (IF: 6.575, ISSN 2072-6694), guest edited by Rick L. Stevens. Cancers is an international, peer-reviewed, open access journal of oncology, published semimonthly online by…

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Submit Your Manuscript for a special issue of Cancers: “Modeling Strategies for Drug Response Prediction in Cancer.”

Increase the visibility of your research in deep learning-based drug response prediction by submitting a manuscript to this special issue of Cancers (IF: 6.575, ISSN 2072-6694), guest edited by Rick L. Stevens. Cancers is an international, peer-reviewed, open access journal of oncology, published semimonthly online by MDPI. This special issue will focus on “Modeling Strategies for Drug Response Prediction in Cancer,” including all aspects of cancer drug response modeling from biological and data considerations to computational and algorithmic considerations. This special issue provides a great opportunity to amplify awareness of your research in this rapidly expanding community. Submission Deadline: July 31, 2023

For additional information and submission details, please see https://anl.app.box.com/s/0pybj8wqmn8npyuuzwhvx8eel64uw0m7. For more information about the NCI-DOE Collaboration, the IMPROVE project or how to contribute—or use—models curated by the IMPROVE team, please contact IMPROVE-AI@nih.gov.

The National Cancer Institute (NCI) is hosting a virtual workshop focused on methods to address data limitations for applying artificial intelligence approaches in cancer research. The Cancer AI Research: Computational Approaches Addressing Imperfect Data workshop will run April 3-4, 2023, from 11 AM - 5 PM ET each day.

The goals of this workshop are to (1) examine the state of the science for AI methods designed to operate on noisy, complex, or low-dimensional data; (2) explore how these methods may be applied to key areas of cancer research; and (3) discuss processes for identifying the biological questions that will motivate further advances in machine learning. This workshop will highlight the importance of leveraging advances across fields to accelerate cancer research and discovery through AI.

This virtual workshop is organized around five sessions that span topics including structure prediction; cellular perturbation; cell-cell interactions and tissue organization; as well as clinical and real-world data. These sessions will cover the cancer research continuum from cancer biology to clinical and implementation research. Sessions will include a mix of presentations and discussions.

This is an open workshop. Please register by March 31 if you’re interested in attending.

The NIH ScHARe social science data repository and research collaboration platform has been launched. This innovative ScHARe platform will:

  • host a wealth of social determinants of health and other social science datasets
  • provide cloud computing tools and secure workspaces to allow easy, low-cost analysis of these data
  • foster collaborations to develop AI bias mitigation strategies and advance health disparity and health care delivery research using big data

The ScHARe platform also offers training through its Think-a-Thon interactive webinar series. Think-a-Thons are for everyone interested in health disparities and health care delivery research, as well as data scientists who want to engage in practical applications of AI or address bias mitigation strategies. All disciplines and levels of background knowledge are welcome!

The first Think-a-Thon is scheduled for Wednesday, February 15 at 2:30 PM (ET). This event will focus on:

  • The basic data science, cloud computing, and AI concepts behind ScHARe
  • Technology benefits and concerns in cloud computing research collaborations

Register today to attend this event! Future Think-a-Thons will foster research collaborations, provide training in the use of cloud resources and advanced artificial intelligence bias mitigation tools, and more! Check out the full webinar schedule

The Virtual Digital Twin Micro Lab was held on April 23, 2020 with participants from more than 40 organizations. Download the video presentations, see the PowerPoint slides and read the breakout discussion notes!

Registration is now open for the 2019 ML-MSM Meeting (October 24-25, 2019) to be held in Bethesda, MD (NIH Campus). The meeting will focus on multiple domain approaches to developing Digital Twins and addressing Human Safety.

Paul Macklin of Indiana University – and MicroLab co-lead of Digital Twin ― co-authored a paper with Argonne National Lab (which includes running many patient simulations on HPC to screen treatment choices). The paper is titled, “Learning-accelerated discovery of immune-tumour interactions,” published in Molecular Systems Design and Engineering on June 7, 2019.