NIH grant: Improving accessibility to multi-modal cancer image analysis and visualization through open-source software development (1R50CA310966-01)

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NIH grant 1R50CA310966-01: Improving accessibility to multi-modal cancer image analysis and visualization through open-source software development
Institute: {'code': 'CA', 'abbreviation': 'NCI', 'name': 'National Cancer Institute', 'admin_org_id': '12500008', 'admin_funding_url': ''}. PI: Cameron  Watson. Award: $119394.
Start: 2026-08-15T00:00:00.
Project Summary/Abstract
The goal of this application is to support Cameron Watson, MS, as a Computational Biologist and software
developer at the Oregon Health and Science University (OHSU) Knight Cancer Institute. With guidance from
Dr. Jeremy Goecks at Moffitt Cancer Center and a highly supportive team of collaborators at OHSU across
computational biology, precision oncology, and machine learning, this Research Software Engineer award
would allow Mr. Watson to dedicate time to developing software for analysis and visualization of multi-modal
cancer datasets in support of the NCI-funded Information Technology in Cancer Research (ITCR) and the
Human Tumor Atlas Network (HTAN). Mr. Watson has extensive experience in open-source software
engineering and cancer data science and is deeply immersed in collaborative cancer research communities.
With funding from this award, Mr. Watson would expand his contributions to ITCR and HTAN by 1) Enhancing
efficiency and intuitive design of multi-modal and multi-sample visualization; 2) Improving accessibility to deep
learning tools and foundation models for cancer research; 3) Implementing open-source and cloud-ready data
formats; and 4) Applying novel tools to advance predictive precision oncology and early detection. The tools
and resources developed through this project will be integrated into the Galaxy platform to enable
management and organization of multi-modal datasets following community-driven data standards, increase
accessibility to data processing and analysis workflows, provide data provenance tracking, prepare data for
application of AI models, and facilitate data sharing following FAIR principles. To accomplish these goals, Mr.
Watson will build upon prior work in the community for spatial and `omics integration across multiple modalities
using deep learning-based approaches, cell tracking, and interactive visualization methodologies. He will apply
best practice principles for open-source software development, data formatting and preparation, and actively
engage with the research community through tutorials, documentation, and workshops to encourage wide-
spread adoption. This work will ultimately empower cancer researchers to access sophisticated tools to
process, analyze, and explore complex cancer imaging datasets without encountering current limitations
related to computational skills or resources.

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