Implementation of neural networks for the identification of single molecules

Benjamin P. Bowen, Allan Scruggs, Jörg Enderlein, Markus Sauer, Neal Woodbury

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

The effectiveness of neural networks and the optimization of parameters for implementing neural networks were evaluated for use in the identification of single molecules according to their fluorescence lifetime. The best network architecture and training parameters were determined for both ideal and nonideal single-molecule fluorescence data. The effectiveness of the neural network is compared to that of the maximum likelihood estimator on the basis of its ability to correctly identify single molecules. For ideal single-molecule data, it was found that the neural networks and the maximum likelihood estimator perform approximately equally well. For nonideal single-molecule fluorescence data, neural networks were able to correctly identify a larger percentage of single-molecule events than the MLE method.

Original languageEnglish (US)
Pages (from-to)4799-4804
Number of pages6
JournalJournal of Physical Chemistry A
Volume108
Issue number21
DOIs
StatePublished - May 27 2004

ASJC Scopus subject areas

  • Physical and Theoretical Chemistry

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