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A Fuzzy Cluster-based Algorithm for Peptide Identification

机译：一种基于模糊簇的肽识别算法

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摘要

Peptide identification is a critical step to un-derstand the proteome in cells and tissue. Typically, high-throughput peptide spectra generated in the MS/MS 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.
机译：肽鉴定是未在细胞和组织中造成蛋白质组的关键步骤。通常，通过肽匹配搜索MS / MS过程中产生的高通量肽光谱抵抗真实蛋白质序列。尽管已经开发了许多自动化算法来帮助识别那些高质量的肽谱匹配（PSM），但缺乏值得信赖的目标PSM仍然是一个公开问题。在本文中，我们设计FC-Ranker算法来计算每个目标PSM的得分。将非负重量分配给每个目标PSM以表明其正确的可能性。特别地，我们提出了一种模糊SVM分类模型和模糊轮廓索引，用于迭代地更新目标PSM的分数。此外，FC-Ranker提供了一种解决目标PSM的不确定性问题的框架，并且可以轻松调整以适应新数据集。

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