Classifying b and y ions in peptide tandem mass spectra

Authors:
Changyong Yu;Guoren Wang;Junjie Wu;Keming Mao
Affiliations:
Key Laboratory of Medical Image Computing, Northeastern University, Ministry of Education, Shenyang, China and College of Information Science and Engineering, Northeastern University, Shenyang, Ch ...;Key Laboratory of Medical Image Computing, Northeastern University, Ministry of Education, Shenyang, China and College of Information Science and Engineering, Northeastern University, Shenyang, Ch ...;College of Information Science and Engineering, Northeastern University, Shenyang, China;College of Information Science and Engineering, Northeastern University, Shenyang, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Year:
2009

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Abstract

In computational proteomics, the peptide identification via interpreting its tandem mass spectrum is an important issue. The classification of b and y ions in the spectrum plays a vital role for improving the accuracy of most existing algorithms. To solve this problem, a classification method based on frequent pattern mining and decision tree is proposed in this paper. First a dataset is established by use of the identified spectrum in which each datum records the ion positions around an ion with b or y type. The discriminative ion frequent patterns (DIFP) of b and y ions are mined with the dataset. And then a decision tree model organizing these DIFPs is proposed for classifying the b and y ions. Finally, we develop an algorithm for the b and y ions classification called B/Y-Classifier. The experimental results demonstrate that an accuracy level of 92% is achieved.