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» Duration learning for analysis of nanopore ionic current blo...
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BMCBI
2007
98views more  BMCBI 2007»
14 years 10 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»
14 years 10 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»
14 years 10 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»
14 years 10 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»
14 years 10 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