离子通道传感器信号处理和机器学习的指令工具

Q3 Social Sciences
P. Sattigeri, Jayaraman J. Thiagarajan, K. Ramamurthy, A. Spanias, M. Banavar, A. Dixit, Jie Fan, Mohit Malu, Kristen Jaskie, Sunil Rao, U. Shanthamallu, V. Narayanaswamy, Sameeksha Katoch
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引用次数: 0

摘要

离子通道传感器有多种应用,包括DNA测序、生物威胁检测和医疗应用。离子通道传感器模拟细胞膜的选择性运输机制,可以在分子水平上检测广泛的分析物。分析物是通过信号模式的变化来感知的。文献中的论文描述了离子通道信号分析的不同方法。在本文中,我们描述了一系列新的离子通道信号分析图形工具,可用于研究和教育。本文重点介绍了这些工具在生物传感器课程中的应用。离子通道传感器的信号处理和机器学习的教学具有挑战性,因为它的多学科内容和学生背景包括物理、化学、生物和工程。本文介绍了为在线仿真环境J-DSP开发的图形化离子通道分析工具。这些工具将在研究生生物传感器课程中通过计算机实验室练习进行整合和评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Instruction Tools for Signal Processing and Machine Learning for Ion-Channel Sensors
Ion Channel sensors have several applications including DNA sequencing, biothreat detection, and medical applications. Ion-channel sensors mimic the selective transport mechanism of cell membranes and can detect a wide range of analytes at the molecule level. Analytes are sensed through changes in signal patterns. Papers in the literature have described different methods for ion channel signal analysis. In this paper, we describe a series of new graphical tools for ion channel signal analysis which can be used for research and education. The paper focuses on the utility of this tools in biosensor classes. Teaching signal processing and machine learning for ion channel sensors is challenging because of the multidisciplinary content and student backgrounds which include physics, chemistry, biology and engineering. The paper describes graphical ion channel analysis tools developed for an on-line simulation environment called J-DSP. The tools are integrated and assessed in a graduate bio-sensor course through computer laboratory exercises.
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来源期刊
CiteScore
1.70
自引率
0.00%
发文量
39
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