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
{"title":"离子通道传感器信号处理和机器学习的指令工具","authors":"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","doi":"10.4018/ijvple.285601","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":53545,"journal":{"name":"International Journal of Virtual and Personal Learning Environments","volume":"15 1","pages":"1-17"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Instruction Tools for Signal Processing and Machine Learning for Ion-Channel Sensors\",\"authors\":\"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\",\"doi\":\"10.4018/ijvple.285601\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":53545,\"journal\":{\"name\":\"International Journal of Virtual and Personal Learning Environments\",\"volume\":\"15 1\",\"pages\":\"1-17\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Virtual and Personal Learning Environments\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijvple.285601\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Virtual and Personal Learning Environments","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijvple.285601","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Social Sciences","Score":null,"Total":0}
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.