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; 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。液体代谢组分析 色谱 - 质谱法（LC/MS）同时测量数千种代谢产物。这 LC/MS代谢组分析数据构成了独特的挑战，这是由于固有的几种特征 匹配特征与已知代谢产物的不确定性，真实零和缺失值的混合，以及 不同的数据分布和依赖模式，妨碍了与其他类型的高级分析的综合分析 维数据。在这项研究中，我们计划通过开发贝叶斯分层模型来解决问题 结合匹配不确定性的网络标记选择，这是一个集成的回归框架 对多部分的OMICS网络的分析，以及一种新颖的建模策略，以应对独特的挑战 代谢网络中缺少值。我们将向大规模的，高影响力应用新开发的方法 代谢组学和转录组学数据可获取新的生物学见解，并为易于使用的软件提供 社区。