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Machine Learning Tools for Discovery and Analysis of Active Metabolic Pathways

用于发现和分析活跃代谢途径的机器学习工具

基本信息

批准号：
9899255
负责人：
ALI SHOJAIE
金额：
$ 33.69万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-04-01 至 2022-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9899255
关键词：
Address Adoption Anabolism Area Biochemical Pathway Biochemical Reaction Biological Biological Assay Cardiovascular Diseases Cell physiology Cells Characteristics Code Communities Complement Complex Computer software Computing Methodologies Data Data Analyses Data Coordinating Center Data Set Deposition Detection Development Diabetes Mellitus Disease Environment Environmental Risk Factor Equilibrium Funding Galaxy Homeostasis Knowledge Label Language Letters Linear Models Machine Learning Malignant Neoplasms Mass Spectrum Analysis Measurement Measures Metabolic Metabolic Pathway Metabolism Methodology Methods Names Network-based Nuclear Magnetic Resonance Pathway interactions Phase Phenotype Plug-in Procedures Process Prognostic Marker Proteomics Reaction Sampling Signal Transduction Software Tools System Technology Testing United States National Institutes of Health Visualization Work base biological systems biomarker discovery data warehouse diagnostic biomarker diverse data experimental study flexibility high dimensionality improved insight interest machine learning method metabolome metabolomics new technology novel novel diagnostics novel marker open source programs public health relevance rapid growth response small molecule statistical and machine learning targeted treatment tool transcriptomics

项目摘要

DESCRIPTION (provided by applicant): This project aims to develop new statistical machine learning methods for metabolomics data from diverse platforms, including targeted and unbiased/global mass spectrometry (MS), labeled MS experiments for measuring metabolic ﬂux and Nuclear Magnetic Resonance (NMR) platforms. Unbiased MS and NMR proﬁling studies result in identifying a large number of unnamed spectra, which cannot be directly matched to known metabolites and are hence often discarded in downstream analyses. The ﬁrst aim develops a novel kernel penalized regression method for analysis of data from unbiased proﬁling studies. It provides a systematic framework for extracting the relevant information from unnamed spectra through a kernel that highlights the similarities and differences between samples, and in turn boosts the signal from named metabolites. This results in improved power in identiﬁcation of named metabolites associated with the phenotype of interest, as well as improved prediction accuracy. An extension of this kernel-based framework is also proposed to allow for systematic integration of metabolomics data from diverse proﬁling studies, e.g. targeted and unbiased MS proﬁling technologies. The second aim pro- vides a formal inference framework for kernel penalized regression and thus complements the discovery phase of the ﬁrst aim. The third aim focuses on metabolic pathway enrichment analysis that tests both orchestrated changes in activities of steady state metabolites in a given pathway, as well as aberrations in the mechanisms of metabolic reactions. The fourth aim of the project provides a uniﬁed framework for network-based integrative analysis of static (based on mass spectrometry) and dynamic (based on metabolic ﬂux) metabolomics measurements, thus providing an integrated view of the metabolome and the ﬂuxome. Finally, the last aim implements the pro- posed methods in easy-to-use open-source software leveraging the R language, the capabilities of the Cytoscape platform and the Galaxy workﬂow system, thus providing an expandable platform for further developments in the area of metabolomics. The proposed software tool will also provide a plug-in to the Data Repository and Coordination Center (DRCC) data sets, where all regional metabolomics centers supported by the NIH Common Funds Metabolomics Program deposit curated data.