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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

基本信息

批准号：
10228099
负责人：
Jian Kang
金额：
$ 34.78万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10228099
关键词：
Address Area Bayesian Modeling Bayesian Network Biochemical Pathway Bioconductor Bioinformatics Biological Biological Markers Biology Cardiovascular Diseases Characteristics Communities Complex Computational Technique Computational algorithm Computer software Data Data Analyses Data Set Dependence Disease Drug Targeting Etiology Funding Ions Isotopes Measurement Measures Mediation Mediator of activation protein Methods Modeling Molecular Weight Network-based Pathway Analysis Pattern Performance Phenotype Property Sampling Statistical Methods Statistical Models Study models System Time Uncertainty United States National Institutes of Health adduct base biological systems biomarker selection feature selection flexibility genome-wide graphical user interface high dimensionality improved insight knowledge base liquid chromatography mass spectrometry metabolomics multidimensional data network models novel novel strategies theories transcriptomics user friendly software vaccinology

项目摘要

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）同时测量数千种代谢物。的 LC/MS代谢组学分析数据提出了独特的挑战，由于几个特点，包括内在的将特征与已知代谢物匹配的不确定性，真零和缺失值的混合，以及不同的数据分布和依赖模式，阻碍了与其他类型的高维度数据在这项研究中，我们计划通过开发贝叶斯分层模型来解决这些问题，网络标记选择，包括匹配的不确定性，一个回归框架，多组学网络的分析，以及一种新的建模策略，以解决独特的挑战，代谢网络中的缺失值。我们将把新开发的方法应用于大规模、高影响的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，社会各界