Xintao Wu
University of Arkansas, AR·Computer Science
Biomedical research now generates data far faster than most labs can analyze it: microscope images, genome-scale measurements of genes, proteins and metabolites, and readouts of how cells produce energy. Methods from data science and artificial intelligence, especially deep learning and foundation models, make it possible to analyze these different data types together, reconstruct metabolic pathways, predict disease severity and nominate therapeutic targets.
- Fields
Computer Science
Biochemistry
Medicine & Clinical Research
- Topics
data science
artificial intelligence
deep learning
- Grants
- 1 known grant, through Feb 2031
What they work on
- Provide data analytics and artificial intelligence services, including customized deep learning solutions, to Phase 2 research project leaders, pilot awardees and other university and regional users of the metabolic research center.
- Maintain and expand computing and storage equipment so the core can deliver services independently and in integrated workflows with the center's Imaging and Bioenergetics Cores.
- Deliver hands-on training in high-performance computing, deep learning for biomedical imaging, and integration of multi-omics data covering genes, proteins and metabolites.
- Support joint analysis of multi-modality biomedical data to reconstruct metabolic pathways, predict disease severity and identify candidate therapeutic targets.
More about the research
Supplying that analytical capability to an entire research center is the purpose of this Data Science Core.
Why it matters
Shared computing power, tailored analysis methods and hands-on training give metabolism researchers in Arkansas the means to extract more from imaging and multi-omics data than they could alone. Such analyses could support reconstruction of metabolic pathways, prediction of disease severity and identification of new therapeutic targets, and they broaden access to artificial intelligence skills for biomedical researchers across the state and region.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Alexis Battle
Johns Hopkins University, MD·Computer Science
Mentor early-stage investigators in study design, data pipelines and quantitative methods for multimodal, longitudinal data in aging and Alzheimer's disease research.
Michael Whitfield
Dartmouth College, NH·Genetics
Enhance the research competitiveness of junior faculty in genomic data science and experimental genomics through mentoring and a supportive research environment.
Jinhua Wang
University of Minnesota, MN·Computer Science
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.
The school
University of Arkansas: 12 labs on PI Pages, $231M a year on research, 179 research doctorates a year.
Reach out
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