# Bayesian Network Biomarker Selection in Metabolomics Data

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $347,778

## Abstract

Metabolomics is one of the major areas of high-throughput biology. Metabolomic profiling by liquid
chromatography-mass spectrometry (LC/MS) measures thousands of metabolites at the same time. The
LC/MS metabolomic profiling data poses unique challenges due to several characteristics including the intrinsic
uncertainty in matching features to known metabolites, the mixing of true zeroes and missing values, and
distinct data distribution and dependency patterns that hamper integrative analysis with other types of high-
dimensional data. In this study, we plan to tackle the problems by developing Bayesian hierarchical models for
network marker selection that incorporates matching uncertainties, a regression framework for integrative
analysis of multipartite omics networks, and a novel modeling strategy to address the unique challenge of
missing values in the metabolic network. We will apply newly developed methods to large-scale, high-impact
metabolomics and transcriptomics data to derive new biological insights, and provide easy-to-use software for
the community.

## Key facts

- **NIH application ID:** 10228099
- **Project number:** 5R01GM124061-05
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Jian Kang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $347,778
- **Award type:** 5
- **Project period:** 2017-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10228099

## Citation

> US National Institutes of Health, RePORTER application 10228099, Bayesian Network Biomarker Selection in Metabolomics Data (5R01GM124061-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10228099. Licensed CC0.

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