Advanced Computational Approaches for NMR Data-mining

NMR 数据挖掘的高级计算方法

• 批准号: 10372268
• 负责人: Zhandong Liu
• 金额: $ 26.75万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10372268
• 关键词: Address; Algorithms; Animal Disease Models; Biological; Biological Markers; Blood; Cardiovascular Diseases; Cells; Chemicals; Clinic; Clinical; Clinical Medicine; Complement; Computer software; Consumption; Data; Data Analyses; Data Set; Development; Diabetes Mellitus; Diagnostic; Disease; Drug toxicity; Early Diagnosis; Frequencies; Health; Human; Knowledge; Left; Libraries; Link; Machine Learning; Malignant Neoplasms; Manuals; Mass Spectrum Analysis; Measures; Medical; Metabolic; Methods; Modeling; Molecular; NMR Spectroscopy; Nature; Neurodegenerative Disorders; Noise; Nuclear Magnetic Resonance; Nutritional; Obesity; Organism; Outcome; Patients; Pharmacologic Substance; Phenotype; Plague; Process; Regulation; Relaxation; Reporting; Reproducibility; Research; Residual state; Resistance; Sampling; Sensitivity and Specificity; Shapes; Signal Transduction; Societies; Sodium Chloride; Spectrum Analysis; Statistical Algorithm; Structure; Temperature; Time; Tissues; Treatment outcome; Urine; Variant; automated algorithm; automated analysis; base; biological systems; biomarker discovery; clinical diagnostics; clinical implementation; clinical practice; computational suite; data mining; experimental analysis; experimental study; high dimensionality; improved; infancy; machine learning method; metabolome; metabolomics; multidimensional data; novel; personalized medicine; phenotypic biomarker; small molecule; stem

ABSTRACT Nuclear magnetic resonance spectroscopy (NMR)-based metabolomics is a powerful method for identifying metabolic perturbations that report on different biological states and sample types. Compared to mass spectrometry, NMR provides robust and highly reproducible quantitative data in a matter of minutes, which makes it very suitable for first-line clinical diagnostics. Although the metabolome is known to provide an instantaneous snap-shot of the biological status of a cell, tissue, and organism, the utilization of NMR in clinical practice is hindered by cumbersome data analysis. Major challenges include high-dimensionality of the data, overlapping signals, variability of resonance frequencies (chemical shift), non-ideal shapes of signals, and low signal-to-noise ratio (SNR) for low concentration metabolites. Existing approaches fail to address these challenges and sample analysis is time-consuming, manually done, and requires considerable knowledge of NMR spectroscopy. Recent developments in the field of sparse methods for machine learning and accelerated convex optimization for high dimensional problems, as well as kernel-based spatial clustering show promise at enabling us to overcome these challenges and achieve fully automated, operator-independent analysis. We are developing two novel, powerful, and automated algorithms that capitalize on these recent developments in machine learning. In Aim 1, we describe ‘NMRQuant’ for automated identification and quantification of annotated metabolites irrespective of the chemical shift, low SNR, and signal shape variability. In Aim 2, we describe ‘SPA-STOCSY’ for automated de-novo identification of molecular fragments of unknown, non- annotated metabolites. Based on substantial preliminary data, we propose to evaluate these algorithms' sensitivity, specificity, stability, and resistance to noise on phantom, biological, and clinical samples, comparing them to current methods. We will validate the accuracy of analyses by experimental 2D NMR, spike-in, and mass spectrometry. The proposed efforts will produce new NMR analytical software for discovery of both annotated and non-annotated metabolites, substantially improving accuracy and reproducibility of NMR analysis. Such analytical ability would change the existing paradigm of NMR-based metabolomics and provide an even stronger complement to current mass spectrometry-based methods. This approach, once thoroughly validated, will enable NMR to reach wide network of applications in biomedical, pharmaceutical, and nutritional research and clinical medicine.

摘要 基于核磁共振波谱（NMR）的代谢组学是一种强有力的方法， 报告不同生物状态和样品类型的代谢扰动。与质量相比 NMR在几分钟内提供了可靠且高度可重复的定量数据， 使其非常适合一线临床诊断。虽然已知代谢组提供了一个 细胞、组织和生物体的生物状态的瞬时快照，核磁共振在临床中的应用， 繁琐的数据分析阻碍了实践。主要挑战包括数据的高维性， 重叠的信号，共振频率的变化性（化学位移），信号的非理想形状，以及低 低浓度代谢物的信噪比（SNR）。现有的方法无法解决这些问题 挑战和样品分析是耗时的，手动完成，并需要相当多的知识， 核磁共振波谱法。机器学习和加速稀疏方法领域的最新进展 高维问题的凸优化，以及基于核的空间聚类显示出希望， 使我们能够克服这些挑战，实现完全自动化、独立于操作员的分析。我们 正在开发两种新的，强大的自动化算法，利用这些最新的发展， 机器学习在目标1中，我们描述了用于自动识别和定量的“NMRQuant”， 注释的代谢物，而不考虑化学位移、低SNR和信号形状可变性。在目标2中， 描述了“SPA-STOCSY”，用于自动从头鉴定未知、非 注释代谢物。基于大量的初步数据，我们建议评估这些算法的 灵敏度、特异性、稳定性和对体模、生物和临床样本的抗噪声性，比较 目前的方法。我们将通过实验2D NMR，spike-in， 质谱分析法来拟议的努力将产生新的核磁共振分析软件，用于发现 注释和非注释代谢物，大大提高了NMR的准确性和再现性 分析.这种分析能力将改变基于NMR的代谢组学的现有范式，并提供 这是对目前基于质谱的方法的更有力的补充。这种方法，一旦彻底 经过验证，将使NMR在生物医学、制药和营养学领域获得广泛的应用 研究和临床医学。