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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages265-274
Number of pages10
Volume5769 LNCS
EditionPART 2
DOIs
StatePublished - 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)03029743
ISSN (Electronic)16113349

Other

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

Fingerprint

Nanopore
Ion Channels
Nanopores
Ions
Chemical elements
Statistical Analysis
Nanoparticles
Statistical methods
Sensing
Signature
Classify
Neural Networks
Neural networks
Distinct
Sensor
Sensors
Processing
Demonstrate

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Konnanath, B., Sattigeri, P., Mathew, T., Spanias, A., Prasad, S., Goryll, M., ... Knee, P. (2009). Acquiring and classifying signals from nanopores and ion-channels. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 5769 LNCS, 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

Acquiring and classifying signals from nanopores and ion-channels. / Konnanath, Bharatan; Sattigeri, Prasanna; Mathew, Trupthi; Spanias, Andreas; Prasad, Shalini; Goryll, Michael; Thornton, Trevor; Knee, Peter.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5769 LNCS PART 2. ed. 2009. p. 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).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 5769 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 5769 LNCS, pp. 265-274, 19th International Conference on Artificial Neural Networks, ICANN 2009, Limassol, Cyprus, 9/14/09. https://doi.org/10.1007/978-3-642-04277-5_27
Konnanath B, Sattigeri P, Mathew T, Spanias A, Prasad S, Goryll M et al. Acquiring and classifying signals from nanopores and ion-channels. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 5769 LNCS. 2009. p. 265-274. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-04277-5_27
Konnanath, Bharatan ; Sattigeri, Prasanna ; Mathew, Trupthi ; Spanias, Andreas ; Prasad, Shalini ; Goryll, Michael ; Thornton, Trevor ; Knee, Peter. / Acquiring and classifying signals from nanopores and ion-channels. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5769 LNCS PART 2. ed. 2009. pp. 265-274 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
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