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.

Original languageEnglish (US)
Title of host publicationArtificial Neural Networks - ICANN 2009 - 19th International Conference, Proceedings
Pages265-274
Number of pages10
EditionPART 2
DOIs
StatePublished - Nov 27 2009
Event19th International Conference on Artificial Neural Networks, ICANN 2009 - Limassol, Cyprus
Duration: Sep 14 2009Sep 17 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5769 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Conference on Artificial Neural Networks, ICANN 2009
CountryCyprus
CityLimassol
Period9/14/099/17/09

Keywords

  • Denoising using wavelets
  • Ion-channel sensors
  • Nanopore devices
  • PCA
  • Sensing using nanopores and neural networks
  • WHT

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Konnanath, B., Sattigeri, P., Mathew, T., Spanias, A., Prasad, S., Goryll, M., Thornton, T., & Knee, P. (2009). Acquiring and classifying signals from nanopores and ion-channels. In Artificial Neural Networks - ICANN 2009 - 19th International Conference, Proceedings (PART 2 ed., pp. 265-274). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5769 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-04277-5_27