Bayesian Network Biomarker Selection in Metabolomics Data

代谢组学数据中的贝叶斯网络生物标志物选择

• 批准号: 10125318
• 负责人: Jian Kang
• 金额: $ 27.35万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10125318
• 关键词: Bayesian; Network; Biomarker; Selection; Metabolomics

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.

代谢组学是高通量生物学的主要领域之一。液体代谢组学分析 色谱-质谱 (LC/MS) 可同时测量数千种代谢物。这 由于包括内在特征在内的多种特征，LC/MS 代谢组学分析数据提出了独特的挑战。 特征与已知代谢物匹配的不确定性、真零值和缺失值的混合，以及 不同的数据分布和依赖性模式阻碍了与其他类型的高 维度数据。在本研究中，我们计划通过开发贝叶斯分层模型来解决这些问题 包含匹配不确定性的网络标记选择，综合回归框架 多部分组学网络的分析，以及一种新颖的建模策略来应对独特的挑战 代谢网络中缺失值。我们将把新开发的方法应用于大规模、高影响力的研究中 代谢组学和转录组学数据以获得新的生物学见解，并提供易于使用的软件 社区。