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» Duration learning for analysis of nanopore ionic current blo...
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BMCBI
2007
98views more  BMCBI 2007»
13 years 4 months ago
Duration learning for analysis of nanopore ionic current blockades
Background: Ionic current blockade signal processing, for use in nanopore detection, offers a promising new way to analyze single molecule properties, with potential implications ...
Alexander G. Churbanov, Carl Baribault, Stephen Wi...
BMCBI
2008
132views more  BMCBI 2008»
13 years 5 months ago
Clustering ionic flow blockade toggles with a Mixture of HMMs
Background: Ionic current blockade signal processing, for use in nanopore detection, offers a promising new way to analyze single molecule properties with potential implications f...
Alexander G. Churbanov, Stephen Winters-Hilt
BMCBI
2007
153views more  BMCBI 2007»
13 years 4 months ago
Analysis of nanopore detector measurements using Machine-Learning methods, with application to single-molecule kinetic analysis
Background: A nanopore detector has a nanometer-scale trans-membrane channel across which a potential difference is established, resulting in an ionic current through the channel ...
Matthew Landry, Stephen Winters-Hilt
BMCBI
2006
91views more  BMCBI 2006»
13 years 4 months ago
Cheminformatics methods for novel nanopore analysis of HIV DNA termini
Background: Channel current feature extraction methods, using Hidden Markov Models (HMMs) have been designed for tracking individual-molecule conformational changes. This informat...
Stephen Winters-Hilt, Matthew Landry, Mark Akeson,...
BMCBI
2007
115views more  BMCBI 2007»
13 years 4 months ago
A novel, fast, HMM-with-Duration implementation - for application with a new, pattern recognition informed, nanopore detector
Background: Hidden Markov Models (HMMs) provide an excellent means for structure identification and feature extraction on stochastic sequential data. An HMM-with-Duration (HMMwD) ...
Stephen Winters-Hilt, Carl Baribault