0000000001035247

AUTHOR

Zehra Cataltepe

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Peptide classification using optimal and information theoretic syntactic modeling

2010

Accepted version of an article published in the journal: Pattern Recognition. Published version available on Sciverse: http://dx.doi.org/10.1016/j.patcog.2010.05.022 We consider the problem of classifying peptides using the information residing in their syntactic representations. This problem, which has been studied for more than a decade, has typically been investigated using distance-based metrics that involve the edit operations required in the peptide comparisons. In this paper, we shall demonstrate that the Optimal and Information Theoretic (OIT) model of Oommen and Kashyap [22] applicable for syntactic pattern recognition can be used to tackle peptide classification problem. We advoca…

VDP::Mathematics and natural science: 400::Information and communication science: 420::Algorithms and computability theory: 4220206 medical engineeringSequence alignment02 engineering and technologySyntactic pattern recognitionInformation theorySubstitution matrix03 medical and health sciencesArtificial IntelligenceVDP::Medical disciplines: 700::Basic medical dental and veterinary science disciplines: 710::Medical molecular biology: 711030304 developmental biologyMathematicsProbability measure0303 health sciencesbusiness.industryPattern recognitionSimilitudeSupport vector machineSignal ProcessingComputer Vision and Pattern RecognitionArtificial intelligencebusinessClassifier (UML)Algorithm020602 bioinformaticsSoftware
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