LEAPS-MPS: Artificial Intelligence Techniques for Automatic NMR Metabolomics Data Processing

该奖项的部分资金来自2021年美国救援计划法案（公法117-2）。在这个由数学和物理科学理事会和化学部资助的项目中，北卡罗来纳州农业技术州立大学（NCAT）的王波教授和他的学生将开发用于分析化学数据的工具，这些工具更容易应用于非专业人员。这些数据与环境和健康研究有关，将使用核磁共振方法获得。数据处理的新方法将使用人工智能（AI）方法。这项研究可以通过提供对复杂数据集的快速分析，对健康监测产生重大影响。这项研究可能会激励PI机构的学生，PI机构是一所历史上的黑人学院或大学（HBCU）。 王教授计划扩大未被充分代表的少数民族（URM）学生对STEM的参与，包括向学生介绍应用数据科学和人工智能方法，这是非常有市场的工作技能，但在本科阶段没有定期教授。 将提供独立的跨学科研究项目的机会，并将开发一个新的课程。将使用应用于1D和2D NMR数据分析的AI技术来改进代谢物的鉴定。 核磁共振具有很高的仪器可靠性，但普遍存在数据过拟合的问题。这种新方法将为复杂光谱中的峰提取提供改进的方法。训练数据可从已发表的文献和王教授自己的研究中免费获得，该方法将用王教授小组记录的数据进行测试。 开发的方法将有助于使代谢组学和其他复杂的基于NMR的研究更广泛的科学家。这项研究将通过课程和课程开发与教育相结合，并推出一个教育网站。还将举办夏季讲习班。 该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in part under the American Rescue Plan Act of 2021 (Public Law 117-2). In this project, funded by the Mathematical and Physical Sciences Directorate and the Chemistry Division, Professor Bo Wang and his students at North Carolina Agricultural & Technical State University (NCAT) will develop tools for analysis of chemical data that would be easier to apply for non-specialists. The data are related to environmental and health studies and will be obtained using Nuclear Magnetic Resonance (NMR) methods. The novel way of data processing will use artificial intelligence (AI) methods. This research could have significant impacts on health monitoring by providing fast analysis of complicated data sets. The research is likely to inspire students at the PI's institution, which is a Historically Black College or University (HBCU). Prof. Wang’s plans for broadening participation in STEM by underrepresented minority (URM) students include introducing students to applied data science and AI methods, which are very marketable job skills but are not taught regularly at the undergraduate level. Opportunities for independent interdisciplinary research projects will be provided and a new course will be developed.The identification of metabolites will be improved using AI techniques applied to analysis of 1D and 2D NMR data. NMR has high instrument reliability, but there is a common problem of data overfitting. This new method will provide improved ways for extracting peaks in complex spectra. Training data are freely available from published literature and Prof. Wang’s own studies, and the method will be tested with data recorded by Professor Wang’s group. Methods developed will help make metabolomics and other sophisticated NMR-based research accessible to a broader range of scientists. This research will be integrated with education through course and curriculum development, and the introduction of an educational website. Summer workshops will also be conducted. ucted.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.