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Keep up with the latest news from the NCI Center for Biomedical Informatics and Information Technology (CBIIT) and the data science communities.

Tumor cells (colored red and brown) are shown in 3-dimensions against a black background. The cells are close together, emphasizing the importance of cell-cell interactions in supporting tumor growth.

Machine Learning Modeling Generates Accurate Insights into RAS-membrane Biology

In RAS-related diseases, such as cancer, mutations in the RAS genes or their regulators render RAS proteins persistently active. Investigating RAS activation events is challenging when using conventional techniques. An unprecedented multiscale platform is using machine learning to change that.

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Apply for Image Bioinformatics Scientist Position Supporting NCI’s Center for Cancer Research Artificial Intelligence Resource

NCI’s Center for Cancer Research invites applicants with image processing, machine learning, and deep learning experience to be considered for a federal image bioinformatics scientist position supporting the Artificial Intelligence Resource. The successful candidate will develop and implement automated imaging and data processing workflows to analyze large image data sets generated by confocal microscopes.

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Commentary Describes How Digital Twins Could Transform Cancer Care

Drs. Emily Greenspan and Eric Stahlberg of NCI’s CBIIT and Frederick National Laboratory for Cancer Research, respectively, recently contributed to an article, “Digital twins for predictive oncology will be a paradigm shift for precision cancer care,” published in Nature Medicine. The commentary examines the vision that members of NCI’s Envisioning Computational Innovations for Cancer Challenges (ECCIC) community have for developing cancer patient digital twins. Such a platform could revolutionize how clinicians and policymakers approach cancer care and further advance precision medicine.

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Latest Podcast Features NCI Leaders Sharing How Data and Tech Shape the Future of Cancer Research

In a recent podcast, NCI leaders from CBIIT and the Small Business Innovation Research Development Center shared how technological developments have enhanced cancer research and have helped usher in new diagnostics, treatments, and patient care.

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NCI’s Division of Cancer Biology Supports Diverse Cancer Data Science That Uses Machine Learning and Artificial Intelligence

A few of NCI’s Division of Cancer Biology grantees recently released publications on topics such as machine learning and artificial intelligence. These research results hold clues to how we research and develop various cancer treatments.

Figure shows the graphical abstract from the recent article by Hong et al., “Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models,” which was published in Cell Reports Medicine. Using this machine learning model, researchers were able to interpret histological slides to identify four key subtypes of endometrial cancer (CNV-H, CNV-L, MSI-H, and POLE).

NCI-funded Machine Learning Gives New Insight into Endometrial Cancer

A new publication using NCI funding and resources shows that a machine learning model, called Panoptes, allowed cancer researchers to reliably predict subtypes of endometrial cancer. Such “computational pathology” offers a useful framework for supporting human pathologists, trimming the labor needed to interpret histological findings to under 4 minutes per slide, and eliminating the time and cost of genetic sequencing.

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Submit Input to NIH and FDA About Next Generation Sequencing and Radiomics Resource Requirements

Cancer researchers and data scientists have the opportunity to provide NIH and the FDA input on the requirements for accelerating clinical applications of next generation sequencing and radiomics (including those using artificial intelligence and machine learning). Responses to these Requests for Information are due Monday, November 1, 2021.

Image of a cloud made up of different container technology icons (i.e. docker, snakemake)

Learn More About the Cloud and Container Technology Driving Cancer Data Science at the Containers and Workflow Interest Group (CWIG) Webinars

The relaunched monthly CWIG webinar series will invite researchers from across the globe to discuss the latest advancements in cloud computing technologies, workflow, tools, and packages.

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NIH Issues RFI for Advancing AI/ML Technologies to Redress Health Disparities: Responses Due July 9

This RFI will inform the development of an NIH initiative on the use of Artificial Intelligence/Machine Learning to address health disparities and inequities and enhance diversity within the AI/ML workforce.

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Read NCI's Four Priority Areas for Artificial Intelligence

NCI Director Dr. Ned Sharpless and CBIIT Director Dr. Tony Kerlavage recently published an article, “The Potential of AI in Cancer Care and Research,” which takes stock of current advances in artificial intelligence and machine learning and outlines some of the areas NCI hopes to explore in greater depth in the future.

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