Acquiring and Classifying Signals from Nanopores and Ion-Channels

  • Authors:

  • Bharatan Konnanath;Prasanna Sattigeri;Trupthi Mathew;Andreas Spanias;Shalini Prasad;Michael Goryll;Trevor Thornton;Peter Knee

  • Affiliations:

  • SenSIP Center and CSSER, Department of Electrical Engineering, Arizona State University, Tempe, USA 85287-5706;SenSIP Center and CSSER, Department of Electrical Engineering, Arizona State University, Tempe, USA 85287-5706;SenSIP Center and CSSER, Department of Electrical Engineering, Arizona State University, Tempe, USA 85287-5706;SenSIP Center and CSSER, Department of Electrical Engineering, Arizona State University, Tempe, USA 85287-5706;SenSIP Center and CSSER, Department of Electrical Engineering, Arizona State University, Tempe, USA 85287-5706;SenSIP Center and CSSER, Department of Electrical Engineering, Arizona State University, Tempe, USA 85287-5706;SenSIP Center and CSSER, Department of Electrical Engineering, Arizona State University, Tempe, USA 85287-5706;SenSIP Center and CSSER, Department of Electrical Engineering, Arizona State University, Tempe, USA 85287-5706

  • Venue:
  • ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
  • Year:
  • 2009

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Abstract

The use of engineered nanopores as sensing elements for chemical and biological agents is a rapidly developing area. The distinct signatures of nanopore-nanoparticle lend themselves to statistical analysis. As a result, processing of signals from these sensors is attracting a lot of attention. In this paper we demonstrate a neural network approach to classify and interpret nanopore and ion-channel signals.