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

基本信息

项目摘要

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使用的障碍。研究人员实验室的研究表明,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)(一种根据数据进行预测的人工智能形式)可以降低这些错误的可能性。因此,该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。所提出的方法将应用于各种传感器类型、尺寸、形状因素和数据结构。如果成功,获得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课件(OCW)平台,以增加教育和劳动力发展机会。计划的活动包括针对高中生的生物传感游戏驱动模拟、针对本科生的传感器机器学习数据归档虚拟讲座和研讨会以及关于机器学习新兴应用的虚拟讲座。高中生和本科生的生物分析、生命和材料科学领域的研究人员的总体职业目标是通过数据驱动工程的概念帮助改变生物传感器的性能,并扩大化学领域的领导地位。为了实现这一目标,该项目的目标是通过整合物理化学过程建模和监督机器学习来改变生物传感器的可靠性。传质限制表面结合反应理论,用于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将实验参数和传质限制表面结合反应理论与监督机器学习相结合的假设。与最先进的校准方法相比,目标分析物分类模型可以减少 1 型和 2 型误差的程度。所提出的方法将应用于基于生物传感器的 RNA、microRNA 和蛋白质目标的可靠检测,并进行基准测试。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特征,这些特征可以支持使用机器对生物分析物浓度进行可靠分类。如果成功,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着提高生物传感器和基于生物传感器的控制的可靠性和可重复性。该奖项反映了 NSF 的法定使命,并通过使用评估被认为值得支持。基金会的智力价值和更广泛的影响审查标准。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
  • 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
  • 通讯作者:
    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
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Blake Johnson其他文献

“I Am Who I Am Because of Here!”
“我就是因为这里才成为我的!”
  • DOI:
  • 发表时间:
    2014
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Elizabeth Levine Brown;M. Kanny;Blake Johnson
  • 通讯作者:
    Blake Johnson
Effectiveness of team-focused CPR on in-hospital CPR quality and outcomes
以团队为中心的心肺复苏对院内心肺复苏质量和结果的有效性
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    2.4
  • 作者:
    David A. Pearson;Nicole Bensen Covell;Benjamin Covell;Blake Johnson;Cate Lounsbury;Mike Przybysz;Anthony Weekes;Michael Runyon
  • 通讯作者:
    Michael Runyon
A comparison of sterilization techniques for production of decellularized intestine in mice.
小鼠脱细胞肠道生产灭菌技术的比较。
  • DOI:
    10.1089/ten.tec.2019.0219
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Carolyn E Gosztyla;Mitchell R. Ladd;A. Werts;W. Fulton;Blake Johnson;C. Sodhi;D. Hackam
  • 通讯作者:
    D. Hackam
JuliaNLSolvers/Optim.jl: v1.2.1
JuliaNLSolvers/Optim.jl:v1.2.1
  • DOI:
    10.5281/zenodo.4340418
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Patrick Kofod Mogensen;J. White;A. N. Riseth;Tim Holy;M. Lubin;C. Stocker;Andreas Noack;Antoine Levitt;C. Ortner;Blake Johnson;Dahua Lin;Kristoffer Carlsson;Yichao Yu;Christopher Rackauckas;Alex Williams;Ben Kuhn;J. Regier;Cossio;R. Rock;Thomas R. Covert;Takafumi Arakaki;Alexey Stukalov;Andrew P. Clausen;Benjamin Deonovic;B. Pasquier;B. Legat;D. MacMillen;Iain Dunning;Jarrett Revels
  • 通讯作者:
    Jarrett Revels
The Role of School Support in Emerging Adulthood: Facilitating a Healthy Transition in Identity
学校支持在成年初期的作用:促进身份的健康转变
  • DOI:
  • 发表时间:
    2010
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Blake Johnson
  • 通讯作者:
    Blake Johnson

Blake Johnson的其他文献

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{{ truncateString('Blake Johnson', 18)}}的其他基金

Collaborative Research: ISS: Real-time Sensing of Extracellular Matrix Remodeling during Fibroblast Phenotype Switching and Vascular Network Formation in Wound Healing
合作研究:ISS:实时感知成纤维细胞表型转换和伤口愈合中血管网络形成过程中的细胞外基质重塑
  • 批准号:
    2126176
  • 财政年份:
    2022
  • 资助金额:
    $ 54.22万
  • 项目类别:
    Standard Grant
EAGER/Collaborative Research: High-throughput, Autonomous Real-time Monitoring of Tissue Mechanical Property Change via Impedimetric Sensor Arrays
EAGER/协作研究:通过阻抗传感器阵列高通量、自主实时监测组织机械性能变化
  • 批准号:
    2141008
  • 财政年份:
    2021
  • 资助金额:
    $ 54.22万
  • 项目类别:
    Standard Grant
EAGER: Non-invasive Sensing of Superficial Organ Tissue via Conforming Multi-parametric Microfluidic Organ Biosensors (MMOBs): Shifting the Paradigm for Organ Assessment
EAGER:通过多参数微流控器官生物传感器 (MMOB) 对浅表器官组织进行非侵入式传感:改变器官评估的范式
  • 批准号:
    1650601
  • 财政年份:
    2016
  • 资助金额:
    $ 54.22万
  • 项目类别:
    Standard Grant

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