Multiple Kernel Fuzzy SVM-Based Data Fusion for Improving Peptide Identification
Autor: | Xinnan Niu, Parimal Samir, Ling Jian, Andrew J. Link, Xijun Liang, Zhonghang Xia |
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Rok vydání: | 2016 |
Předmět: |
0301 basic medicine
Multiple kernel learning PeptideProphet business.industry Computer science Applied Mathematics Sensor fusion Machine learning computer.software_genre Fuzzy logic Task (project management) 03 medical and health sciences Identification (information) ComputingMethodologies_PATTERNRECOGNITION 030104 developmental biology Software Kernel (statistics) Genetics Artificial intelligence business computer Biotechnology |
Zdroj: | IEEE/ACM Transactions on Computational Biology and Bioinformatics. 13:804-809 |
ISSN: | 1545-5963 |
Popis: | SEQUEST is a database-searching engine, which calculates the correlation score between observed spectrum and theoretical spectrum deduced from protein sequences stored in a flat text file, even though it is not a relational and object-oriental repository. Nevertheless, the SEQUEST score functions fail to discriminate between true and false PSMs accurately. Some approaches, such as PeptideProphet and Percolator, have been proposed to address the task of distinguishing true and false PSMs. However, most of these methods employ time-consuming learning algorithms to validate peptide assignments [1]. In this paper, we propose a fast algorithm for validating peptide identification by incorporating heterogeneous information from SEQUEST scores and peptide digested knowledge. To automate the peptide identification process and incorporate additional information, we employ ${\ell}_2$ multiple kernel learning MKL to implement the current peptide identification task. Results on experimental datasets indicate that compared with state-of-the-art methods, i.e., PeptideProphet and Percolator, our data fusing strategy has comparable performance but reduces the running time significantly. |
Databáze: | OpenAIRE |
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