Instruction tools for signal processing and machine learning for ion-channel sensors

Prasanna Sattigeri, Jayaraman Thiagarajan, Karthikeyan Ramamurthy, Andreas Spanias, Mahesh Banavar, Abhinav Dixit, Jie Fan, Mohit Malu, Kristen Jaskie, Sunil Rao, Uday Shanthamallu, Vivek Narayanaswamy, Sameeksha Katoch

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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, the authors 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 these 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 online simulation environment called J-DSP. The tools are integrated and assessed in a graduate bio-sensor course through computer laboratory exercises.

Original languageEnglish (US)
JournalInternational Journal of Virtual and Personal Learning Environments
Issue number1
StatePublished - 2022


  • Ion-Channel Signal
  • JDSP
  • Signal Processing

ASJC Scopus subject areas

  • Education
  • Computer Science Applications


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