Linked Life Data

20865491

Source:http://linkedlifedata.com/resource/pubmed/id/20865491

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2010-9-24
pubmed:abstractText
Pattern recognition-informed (PRI) feedback using channel current cheminformatics (CCC) software is shown to be possible in "real-time" experimental efforts. The accuracy of the PRI classification is shown to inherit the high accuracy of our offline classifier: 99.9% accuracy in distinguishing between terminal base pairs of two DNA hairpins. The pattern recognition software consists of hidden Markov model (HMM) feature extraction software, and support vector machine (SVM) classification/ clustering software that is optimized for data acquired on a nanopore channel detection system. For general nanopore detection, the distributed HMM and SVM processing used here provides a processing speedup that allows pattern recognition to complete within the time frame of the signal acquisition - where the sampling is halted if the blockade signal is identified as not in the desired subset of events (or once recognized as nondiagnostic in general). We demonstrate that Nanopore Detection with PRI offers significant advantage when applied to data acquisition on antibody-antigen system, or other complex biomolecular mixtures, due to the reduction in wasted observation time on eventually rejected "junk" (nondiagnostic) signals.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
0065-2598
pubmed:author
pubmed:issnType
Print
pubmed:volume
680
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
99-108
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Pattern recognition-informed feedback for nanopore detector cheminformatics.
pubmed:affiliation
Department of Computer Science, University of New Orleans, New Orleans, LA 70148, USA.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural