title: Peptide identification based on fuzzy classification and clustering.
abstract: BACKGROUND
  The 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.
  
  
  RESULTS
  A 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.
  
  
  CONCLUSIONS
  Our 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.
authors.forename: Xijun
authors.initials: X
authors.lastname: Liang
authors.forename: Zhonghang
authors.initials: Z
authors.lastname: Xia
authors.forename: Xinnan
authors.initials: X
authors.lastname: Niu
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Link
authors.forename: Liping
authors.initials: L
authors.lastname: Pang
authors.forename: Fang-Xiang
authors.initials: FX
authors.lastname: Wu
authors.forename: Hongwei
authors.initials: H
authors.lastname: Zhang