Machine Learning Based Differential Mobility Spectrometry Library Development

基于机器学习的差分迁移谱库开发

• 批准号: 10696533
• 负责人: Jacob Golde
• 金额: $ 27.57万
• 依托单位: VOX BIOMEDICAL LLC
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10696533
• 关键词: Analytical Chemistry; Benchmarking; Biological Markers; Breath Tests; Cannabinoids; Cells; Characteristics; Chemicals; Classification; Clinical; Communicable Diseases; Complex; Data; Data Analytics; Detection; Development; Diabetes Mellitus; Dimensions; Disease; Environment; Exhalation; Gas Chromatography; Generations; Goals; Grant; Infection; Injections; Ions; Kidney Diseases; Learning; Libraries; Linear Algebra; Liver diseases; Lung; Machine Learning; Malignant Neoplasms; Mass Fragmentography; Measurement; Measures; Metabolic Diseases; Metabolism; Methods; Modality; Modeling; Molecular; Molecular Analysis; Molecular Structure; NMR Spectroscopy; National Institute of Drug Abuse; Neural Network Simulation; Opioid; Performance; Process; Property; Psychotropic Drugs; Resources; Sampling; Screening for cancer; Signal Transduction; Source; Spectrometry; Spectrum Analysis; Stereoisomer; Techniques; Testing; Time; Training; Travel; Ultrafine; Work; biomarker discovery; biomedical scientist; chemical standard; commercialization; deep neural network; detection method; drug testing; early detection biomarkers; experimental study; instrument; interest; multimodality; multitask; neural network architecture; novel; portability; signal processing; skills; success; volatile organic compound

Project Summary The goal of the project proposed is to develop a gas chromatography and differential mobility spectrometry (GC/DMS) molecular identification library for volatile organic compounds (VOCs) using a deep neural network approach. Vox Biomedical scientists will test the hypothesis that a novel, multi-task neural network architecture can predict characteristics of an previously unseen analyte from its GC/DMS spectrum. Vox Biomedical is in the process of commercializing the GC/DMS based microAnalyzer instrument, developed at DRAPER, for detecting the presence of psychoactive drugs and disease through exhaled breath analysis. While drug detection consists of measuring the concentrations in the exhaled breath of compounds whose identity is well known (such as psychoactive opioids and cannabinoids), exhaled breath disease detection is focused on characterization of a particular disease’s exhaled volatile organic compound signature. Volatile organic compounds (VOCs) are byproducts of cellular metabolism that travel from cells throughout the body to the lungs, where they are efficiently exhaled in the breath. VOCs have become of interest as biomarkers of metabolic diseases such as cancer, kidney disease and diabetes. The current generation of exhaled breath VOC based disease detection methods rely on gas chromatography and mass spectrometry (GC/MS), which, while highly sensitive, is a complex analytical modality that is expensive, slow, and must be operated by skilled professionals. The microAnalyzer instrument’s inherent portability, ease of use, and ability to obtain results at the point-of- measurements make it an ideal instrument for breath-based disease detection. However, the currently a GC/DMS peak can only be identified through characterization of a chemical standard or by performing confirmatory GC/MS analysis using a similar sample. This makes biomarker discovery a resource and time intensive process. The creation of a VOC chemical identity library, as would result from successful completion of the proposed project, will allow the identity of samples introduced to the microAnalyzer instrument to be predicted without the need for confirmatory standard characterization or GC/MS work. This will make biomarker discovery for disease a less resource intensive process expediting the discovery and confirmation of biomarkers for early-stage disease detection, ultimately saving lives.

项目摘要 该项目的目标是开发一种气相色谱和差示迁移率光谱 (GC/DMS）分子鉴定库，用于使用深度神经网络的挥发性有机化合物（VOCs） approach. Vox生物医学科学家将测试一种新的多任务神经网络架构的假设， 可以从GC/DMS光谱中预测以前看不见的分析物的特征。 Vox Biomedical正在将基于GC/DMS的microAnalyzer仪器商业化， 在德雷珀，用于通过呼出气体分析检测精神药物和疾病的存在。 而药物检测包括测量呼出气体中的化合物的浓度， 身份是众所周知的（如精神活性阿片类药物和大麻素），呼出气疾病检测， 专注于特定疾病呼出的挥发性有机化合物特征的表征。挥发性 有机化合物（VOC）是细胞代谢的副产品，从细胞到全身， 肺部，在那里它们被有效地呼出。VOC已经成为感兴趣的生物标志物， 代谢性疾病，如癌症、肾病和糖尿病。当前产生的呼出气VOC 基于的疾病检测方法依赖于气相色谱和质谱（GC/MS）， 高灵敏度是一种复杂的分析模式，其昂贵、缓慢，并且必须由熟练的操作人员操作。 专业人士 microAnalyzer仪器固有的便携性、易用性和在检测点获得结果的能力， 测量使其成为基于呼吸的疾病检测的理想仪器。目前，A GC/DMS峰只能通过表征化学标准品或通过执行 使用类似样品进行确证性GC/MS分析。这使得生物标志物的发现成为一种资源和时间 密集的过程。建立挥发性有机化合物化学特性资料库， 将允许引入microAnalyzer仪器的样品的身份， 无需确证性标准品表征或GC/MS工作即可预测。这将使生物标志物 疾病的发现是一个资源密集程度较低的过程，加快了生物标志物的发现和确认 用于早期疾病检测，最终挽救生命。