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Indexed by:会议论文

Date of Publication:2012-10-04

Included Journals:EI、CPCI-S、Scopus

Page Number:602-609

Key Words:peptide identification; peptide spectrum matches (PSMs); fuzzy clustering; fuzzy support vector machine (SVM)

Abstract:Peptide identification is a critical step to understand the proteome in cells and tissue. Typically, high-throughput peptide spectra generated in the MSIMS procedure are searched against real protein sequences by peptide matching. Although a number of automated algorithms have been developed to help identifying those high quality of peptide spectrum matches (PSMs), lack of trustworthy target PSMs remains an open problem. In this paper, we design the FC-Ranker algorithm to calculate the score of each target PSM. A nonnegative weight is assigned to each target PSM to indicate its likelihood of being correct. Particularly, we proposed a fuzzy SVM classification model and a fuzzy silhouette index for iteratively updating the scores of target PSMs. Furthermore, FC-Ranker provides a framework for tackling the problem of uncertainty of target PSMs, and it can be easily adjusted to adapt new datasets.