Jinhua Wang
University of Minnesota, MN·Computer Science
Cancer research now produces huge amounts of data. Tumors are profiled by many 'omics' measurements of genes, RNA and proteins, alongside hospital records and clinical trial data.
- Fields
Computer Science
Medicine & Clinical Research
Genetics
- Topics
cancer informatics
bioinformatics
clinical informatics
- Grants
- 1 known grant, through Jan 2029
What they work on
- Develop new computational methods for single-cell multi-omics and spatial transcriptomics imaging, which maps where genes are active in tumor tissue, together with deep learning models that predict cancer outcomes.
- Improve cancer clinical trial recruitment by analyzing data on the factors behind enrollment success and building decision models to boost accrual.
- Build data integration infrastructure and methods toward cancer patient 'digital twins', computer models of individual patients that support predictive oncology.
- Maintain a FAIR, AI-ready data environment and a genomics repository linking genomic data to clinical health records for cancer center researchers.
More about the research
Making sense of all this requires secure storage, data integration and new analysis methods. This grant supports the Cancer Informatics Shared Resource (CISR) at the Masonic Cancer Center, a core facility led by Dr. Wang that serves cancer researchers across the center.
Why it matters
Better tools to combine molecular and clinical data could help cancer researchers find patterns that guide precision treatment. Faster clinical trial enrollment could bring new cancer therapies to patients sooner. Patient 'digital twins' may eventually help predict how an individual patient's cancer will respond, supporting both research and patient care.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Yili Zhang
Georgetown University, DC·Computer Science
Develop a large language model–based natural language processing pipeline that extracts immune-related adverse event evidence, type and grade from de-identified clinical notes, creating a reusable annotated corpus.
Gabriel Wardi
University of California-San Diego, CA·Medicine & Clinical Research·Early-career
Identify clinical phenotypes, or distinct patient subgroups, of sepsis that develops after hospital admission, using deep-learning analysis of electronic health record data.
Matthew Churpek
University of Wisconsin-Madison, WI·Medicine & Clinical Research
Improve early identification and risk stratification of infected hospital patients by applying natural language processing and deep learning to structured and free-text electronic health record data from multiple centers.
The school
University of Minnesota: 88 labs on PI Pages, $1.4B a year on research, 722 research doctorates a year.
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