# Interpretable Bayesian Non-linear statistical learning models for multi-omics data integration

> **NIH NIH R35** · UNIVERSITY OF MINNESOTA · 2024 · $374,672

## Abstract

Project Summary
Recent technological advances have enabled the production of vast amounts of diverse multi-omics data types (e.g.,
genomics, epigenomics, proteomics, transcriptomics) of complex diseases such as cancer, cardiovascular diseases
and neurodegenerative disorders. The integration of multi-omics data from those heterogeneous diseases can help
in unraveling the underlying biological mechanisms at multiple omics data levels, in improving prediction of clinical
outcomes, and to transform medicine, but at the same time presents significant challenges to identify important
biomarkers from a large size of heterogeneous molecular data points (i.e. hundreds of thousands). We will
develop and apply novel and powerful Bayesian statistical learning methods that will capture linear and nonlinear
relationships of multi-omics data. The methods will be used to identify i) important predictive pathways and
their corresponding important molecules; ii) clinically meaningful molecular disease subtypes, and iii) predictive
and prognostic biomarkers that contribute to the joint association (or regulatory networks) between omics data
types. The proposed method will be applied to multiple publicly available datasets such as The Cancer Genome
Atlas, dbGAP, and Genotype-Tissue Expression, and to non public data sets obtained from our collaborators. We
will develop robust, computationally efficient, and user-friendly software free of charge for the application of our
methods.

## Key facts

- **NIH application ID:** 10931642
- **Project number:** 5R35GM150537-02
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Thierry Chekouo Tekougang
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $374,672
- **Award type:** 5
- **Project period:** 2023-09-20 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10931642

## Citation

> US National Institutes of Health, RePORTER application 10931642, Interpretable Bayesian Non-linear statistical learning models for multi-omics data integration (5R35GM150537-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10931642. Licensed CC0.

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