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2010 Ninth International Conference on Machine Learning and Applications
Peptide Sequence Tag-Based Blind Identification-based SVM Model
Washington, D.C., USA
December 12-December 14
ISBN: 978-0-7695-4300-0
| ASCII Text | x | ||
| Hui Li, Chunmei Liu, Xumin Liu, Macire Diakite, Legand Burge, Abdul-Aziz Yakubu, William Southerland, "Peptide Sequence Tag-Based Blind Identification-based SVM Model," Machine Learning and Applications, Fourth International Conference on, pp. 979-984, 2010 Ninth International Conference on Machine Learning and Applications, 2010. | |||
| BibTex | x | ||
| @article{ 10.1109/ICMLA.2010.156, author = {Hui Li and Chunmei Liu and Xumin Liu and Macire Diakite and Legand Burge and Abdul-Aziz Yakubu and William Southerland}, title = {Peptide Sequence Tag-Based Blind Identification-based SVM Model}, journal ={Machine Learning and Applications, Fourth International Conference on}, volume = {0}, year = {2010}, isbn = {978-0-7695-4300-0}, pages = {979-984}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICMLA.2010.156}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Machine Learning and Applications, Fourth International Conference on TI - Peptide Sequence Tag-Based Blind Identification-based SVM Model SN - 978-0-7695-4300-0 SP979 EP984 A1 - Hui Li, A1 - Chunmei Liu, A1 - Xumin Liu, A1 - Macire Diakite, A1 - Legand Burge, A1 - Abdul-Aziz Yakubu, A1 - William Southerland, PY - 2010 KW - Tandem mass spectrum KW - Support Vector Machine KW - Context free grammar VL - 0 JA - Machine Learning and Applications, Fourth International Conference on ER - | |||
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.
Index Terms:
Tandem mass spectrum, Support Vector Machine, Context free grammar
Citation:
Hui Li, Chunmei Liu, Xumin Liu, Macire Diakite, Legand Burge, Abdul-Aziz Yakubu, William Southerland, "Peptide Sequence Tag-Based Blind Identification-based SVM Model," icmla, pp.979-984, 2010 Ninth International Conference on Machine Learning and Applications, 2010
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