Advanced Computational Approaches for NMR Data-mining

NMR 数据挖掘的高级计算方法

• 批准号: 9889134
• 负责人: Zhandong Liu
• 金额: $ 35.66万
• 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 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9889134
• 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