Thierry Chekouo Tekougang
University of Minnesota, MN·Mathematics & Statistics
Complex diseases such as cancer, heart disease and neurodegenerative disorders leave traces at many molecular levels at once: in DNA (genomics), in chemical tags on DNA (epigenomics), in which genes are switched on (transcriptomics) and in proteins (proteomics). Measuring all of these together is called multi-omics.
- Fields
Mathematics & Statistics
Computer Science
Genetics
- Topics
Bayesian statistics
multi-omics integration
statistical learning
- Grants
- 1 known grant, through Jul 2028
What they work on
- Develop Bayesian statistical learning models that capture both linear and nonlinear relationships across genomic, epigenomic, transcriptomic and proteomic measurements so several omics layers can be analyzed jointly.
- Identify biological pathways, and the key molecules within them, that predict clinical outcomes, by applying the new integrative Bayesian models to multi-omics patient data.
- Define clinically meaningful molecular subtypes of heterogeneous diseases such as cancer by grouping patients on their combined multi-omics profiles.
- Find predictive and prognostic biomarkers underlying regulatory networks between omics data types, using The Cancer Genome Atlas (TCGA), dbGaP, GTEx and collaborator datasets.
- Build and distribute free, computationally efficient software implementing the multi-omics integration methods.
More about the research
Combining these layers could reveal how a disease works and help predict how patients will do. The difficulty is statistical: the datasets hold hundreds of thousands of measurements, and the few molecules that matter are hard to pick out.
Why it matters
Researchers studying cancer, cardiovascular disease and neurodegenerative disorders could gain better tools for pinpointing which molecules and pathways drive disease. Molecular subtypes and predictive biomarkers found this way may support more accurate diagnosis and more personalized treatment decisions. Freely released software would let other labs apply the methods to their own multi-omics datasets.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Jiebiao Wang
University of Pittsburgh, PA·Mathematics & Statistics
Improve estimation of cell-type fractions in bulk brain tissue by building an ensemble over existing deconvolution methods and incorporating the hierarchical relationships among brain cell types.
Liang Chen
University of Southern California, CA·Genetics
Relate NMD's fine-tuning of gene expression to the genes cancer cells depend on, using NMD residual signatures built from DepMap cell line data and tested for predictive power in TCGA tumor samples.
Junzhou Huang
University of Texas at Arlington, TX·Computer Science
Develop multi-modal deep learning methods, including large language models, to predict the functions of thousands of poorly annotated genes and support analysis of gene-gene synergies.
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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