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

获得可靠的生物传感器可以通过帮助当前和未来的流行病管理来改变公共卫生。 然而，生物传感器的可靠性（例如假阳性（1型）和假阴性（2型）诊断）仍然是广泛的工业和临床应用的障碍。 在研究者实验室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），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概率。 因此，该CAREER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。 所提出的方法将应用于各种传感器类型、尺寸、形状因子和数据结构。如果成功，可靠的生物传感器可以促进生物制造创新，并提高当前和新兴诊断方法的速度和准确性。 该项目的教育目标是创建一个交互式开放式课程（OCW）平台，以增加城市服务不足学生在医疗保健和数据科学界面上的教育和劳动力发展机会。 计划中的活动包括高中生生物传感中的游戏驱动模拟，本科生传感器机器学习数据处理的虚拟讲座和研讨会，以及高中和本科生生物分析，生命和材料科学中机器学习新兴应用的虚拟讲座。研究者的首要职业目标是通过数据驱动的化学工程概念帮助改变生物传感器性能，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地位。 为了实现这一目标，该项目的目标是通过整合理化过程建模和监督ML来改变生物传感器的可靠性。其核心方法是将监督机器学习和传质限制表面结合反应理论相结合，通过生物传感器时间序列数据提高生物分析物定量的可靠性。 该项目将测试以下假设：将实验参数和传质限制表面结合反应理论与用于目标分析物分类的监督机器学习模型相结合，可以减少相对于最先进的校准方法的1型和2型错误的程度。所提出的方法将应用于可靠的基于生物传感器的RNA、microRNA和蛋白质靶点检测，并以标准临床生物分析方法为基准。这项工作将确定目标结合，非特异性结合和生物传感器时间序列数据中生物传感器漂移的新数据和模型驱动特征，这些特征可以支持使用机器学习对生物分析物浓度进行可靠分类。 如果成功的话，识别生物传感器时间序列数据中的目标结合和干扰输入的特征可以显著提高生物传感器和基于生物传感器的控制的可靠性和再现性。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.