Peptide identification based on fuzzy classification and clustering

Proteome Science

Research

Zhonghang Xia¹  Andrew J Link²  Xinnan Niu²  Fang-Xiang Wu³  Liping Pang⁴  Hongwei Zhang⁴  Xijun Liang⁴ 

[1] Dept. of Computer Science, Western Kentucky University, 42101, Bowling Green, KY, USA;Dept. of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, 37232, Nashville, TN, USA;Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Dr, S7N 5A9, Saskatoon, SK, Canada;School of Mathematical Sciences, Dalian University of Technology, 116024, Dalian, China;

关键词: Peptide identification;    Peptide spectrum matches (PSMs);    Fuzzy support vector machine (SVM);    Fuzzy silhouette;   

DOI  :  10.1186/1477-5956-11-S1-S10

来源: Springer

【 摘 要 】

BackgroundThe sequence database searching has been the dominant method for peptide identification, in which a large number of peptide spectra generated from LC/MS/MS experiments are searched using a search engine against theoretical fragmentation spectra derived from a protein sequences database or a spectral library. Selecting trustworthy peptide spectrum matches (PSMs) remains a challenge.ResultsA novel scoring method named FC-Ranker is developed to assign a nonnegative weight to each target PSM based on the possibility of its being correct. Particularly, the scores of PSMs are updated by using a fuzzy SVM classification model and a fuzzy silhouette index iteratively. Trustworthy PSMs will be assigned high scores when the algorithm stops.ConclusionsOur experimental studies show that FC-Ranker outperforms other post-database search algorithms over a variety of datasets, and it can be extended to solve a general classification problem with uncertain labels.

【 授权许可】

Unknown   
© Liang et al; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.