CAREER: Transforming Biosensor Reliability using Sensor Time-series Data and Physics-based Machine Learning

职业：使用传感器时间序列数据和基于物理的机器学习改变生物传感器的可靠性

• 批准号: 2144310
• 负责人: Blake Johnson
• 金额: $ 54.22万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-15 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144310&HistoricalAwards=false
• 关键词: CAREER; Transforming; Biosensor; Reliability; using

Access to reliable biosensors could transform public health by aiding ongoing and future pandemic management. However, biosensor reliability (e.g. false positive (type 1) and false negative(type 2) diagnoses) remains a barrier to widespread industrial and clinical use. Preliminary work performed in the Investigator’s lab suggests that using biosensor time series (TS) data and physics-based supervised Machine Learning (ML), a form of artificial intelligence that makes predictions from data, can reduce the probability of these errors. Thus, the research goal of this CAREER project is to examine the integration of machine learning and chemical engineering domain knowledge for improving biosensor reliability and performance. The proposed methodology will be applied across various sensor types, sizes, form factors, and data structures. If successful, access to reliable biosensors could catalyze biomanufacturing innovations and improve the speed and accuracy of current and emerging diagnostic methods. The education goal of this project is to create an interactive Open Course Ware (OCW) platform to increase education and workforce development opportunities at the interface of healthcare and data sciences for urban-underserved students. Planned activities include Gaming-driven Simulations in Biosensing for High School Students, a Virtual Lecture and Workshop on Data Archiving for Sensor Machine Learning for Undergraduate Students and Virtual Lectures on Emerging Applications of Machine Learning in the Bioanalytical, Life, and Materials Sciences for High School and Undergraduate Students. The investigator’s overarching career goal is to help transform biosensor performance through concepts in data-driven chemical engineering and expand the leadership of underrepresented groups in emerging data-driven life sciences industries. In keeping with this goal, the objective of this project is to transform the reliability of biosensors through the integration of physiochemical process modeling and supervised ML. The central approach is to integrate supervised machine learning and mass transfer-limited surface binding reaction theory for improving the reliability of bioanalyte quantification via biosensor time-series data. This project will test the hypothesis that integrating experimental parameters and mass transfer-limited surface binding reaction theory with supervised machine learning models for target analyte classification can reduce the extent of type 1 and 2 errors relative to state-of-the-art calibration methods. The proposed methodology will be applied to reliable biosensor-based detection of RNA, microRNA, and protein targets and benchmarked against standard clinical bioanalytical methods. This work will identify new data- and model-driven features of target binding, nonspecific binding, and biosensor drift in biosensor time-series data that can support the reliable classification of bioanalyte concentration using machine learning. If successful, identifying features of target binding and interfering inputs in biosensor time-series data could significantly improve the reliability and reproducibility of biosensors and biosensor-based controls.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.

获得可靠的生物传感器可以通过有助于持续和未来的大流行管理来改变公共卫生。但是，生物传感器的可靠性（例如假阳性（1型）和假阴性（2型）诊断）仍然是宽度宽度工业和临床使用的障碍。在研究者实验室中进行的初步工作表明，使用生物传感器时间序列（TS）数据和基于物理的监督机器学习（ML），这是一种从数据中进行预测的人工智能形式，可以降低这些错误的可能性。这是这个职业项目的研究目标是研究机器学习和化学工程领域知识的整合，以提高生物传感器的可靠性和性能。所提出的方法将在各种传感器类型，大小，形式因素和数据结构上应用。如果成功的话，获得可靠的生物传感器可以催化生物制造创新，并提高该项目的教育目标的速度和准确性，那就是创建一个互动的开放式课程（OCW）平台，以增加医疗保健和数据科学界面的教育和劳动力发展机会。计划中的活动包括针对高中生的生物传感的游戏驱动的模拟，针对本科生的传感器机器学习数据归档的虚拟讲座和研讨会以及有关在生物分析，生活和材料科学中的机器学习应用的虚拟讲座，用于高中和学生的生物学科学。研究者的总体职业目标是通过数据驱动的化学工程概念来帮助改变生物传感器的性能，并扩大新兴数据驱动的生命科学行业中代表性不足的群体的领导。为了与这个目标保持一致，该项目的目的是通过整合生理化学过程建模和监督ML来改变BIOSENR的可靠性。中心方法是通过生物传感器时间序列数据整合监督的机器学习和传质限制的表面结合反应理论，以提高生物分析物定量的可靠性。该项目将检验以下假设：将实验参数和质量转移限制的表面结合反应理论与监督的机器学习模型进行目标分析物分类可以减少相对于最先进的校准方法的1和2误差的程度。提出的方法将应用于可靠的生物传感器检测RNA，microRNA和蛋白质靶标，并根据标准的临床生物分析方法进行基准测试。这项工作将确定生物传感器时间序列数据中目标结合，非特异性结合和生物传感器漂移的新数据和模型驱动的特征，这些数据可以支持使用机器学习对生物分析物浓度的可靠分类。如果成功，则识别生物传感器时间序列数据中目标结合和干扰输入的特征可以显着提高生物传感器和基于生物传感器的控制的可靠性和复制。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力和更广泛影响的评估来审查Criteria，通过评估来通过评估来获得支持。

项目成果

Rapid, autonomous high-throughput characterization of hydrogel rheological properties via automated sensing and physics-guided machine learning

• DOI: 10.1016/j.apmt.2022.101720
• 发表时间: 2023-02
• 期刊: Applied Materials Today
• 影响因子: 8.3
• 作者: Junru Zhang;Yang Liu;Durga Chandra Sekhar.P;Manjot Singh;Yuxin Tong;Ezgi Kucukdeger;H. Yoon;Alexander P. Haring;M. Roman;Zhenyu Kong;Blake N. Johnson