NDLI: Peptide Sequence Tag-Based Blind Identification-based SVM Model

Content Provider	IEEE Xplore Digital Library
Author	Hui Li Chunmei Liu Xumin Liu Diakite, M. Burge, L. Yakubu, A. Southerland, W.
Copyright Year	2010
Abstract	Identifying the ion types for a mass spectrum is essential for interpreting the spectrum and deriving its peptide sequence. In this paper, we proposed a novel method for identifying ion types and deriving matched peptide sequences for tandem mass spectra. We first divided our dataset into a training set and a testing set and then preprocessed the data using a Support Vector Machine and a 5-fold cross validation based dual denoting model. Then we constructed a syntax tree and generated a rule set to match the mass values from experimental mass spectra with the mass spectral values from corresponding theoretical mass spectra. Finally we applied the proposed algorithm to a tandem mass spectral dataset consisting of 2656 spectra from yeast. Compared with other methods, the experimental results showed that the proposed method can effectively filter noise and successfully derive peptide sequences.
Starting Page	979
Ending Page	984
File Size	349304
Page Count	6
File Format	PDF
ISBN	9781424492114
DOI	10.1109/ICMLA.2010.156
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Proteins Accuracy Tandem mass spectrum Peptides Noise Context free grammar Amino acids Support Vector Machine
Content Type	Text
Resource Type	Article

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