Using dynamic programming to create isotopic distribution maps from mass spectra | Zendy

Sean McIlwain | Zendy; David Page | Zendy; Edward L. Huttlin | Zendy; Michael R. Sussman | Zendy

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Using dynamic programming to create isotopic distribution maps from mass spectra

Author(s) -

Sean McIlwain,

David Page,

Edward L. Huttlin,

Michael R. Sussman

Publication year - 2007

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btm198

Subject(s) - classifier (uml) , probabilistic logic , computer science , false positive rate , dynamic programming , pattern recognition (psychology) , artificial intelligence , probability distribution , probabilistic classification , word error rate , spectral line , data mining , machine learning , algorithm , statistics , mathematics , support vector machine , physics , naive bayes classifier , astronomy

This article presents a method to identify the isotopic distributions within a mass spectrum using a probabilistic classifier supplemented with dynamic programming. Such a system is needed for a variety of purposes, including generating robust and meaningful features from mass spectra to be used in classification.

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