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NBPMF: Novel Peptide Mass Fingerprinting Based on Network Inference

NBPMF: Novel Peptide Mass Fingerprinting Based on Network Inference

Zhewei Liang, Gilles Lajoie, Kaizhong Zhang
Copyright: © 2017 |Volume: 11 |Issue: 4 |Pages: 25
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781522511724|DOI: 10.4018/IJCINI.2017100103
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MLA

Liang, Zhewei, et al. "NBPMF: Novel Peptide Mass Fingerprinting Based on Network Inference." IJCINI vol.11, no.4 2017: pp.41-65. http://doi.org/10.4018/IJCINI.2017100103

APA

Liang, Z., Lajoie, G., & Zhang, K. (2017). NBPMF: Novel Peptide Mass Fingerprinting Based on Network Inference. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 11(4), 41-65. http://doi.org/10.4018/IJCINI.2017100103

Chicago

Liang, Zhewei, Gilles Lajoie, and Kaizhong Zhang. "NBPMF: Novel Peptide Mass Fingerprinting Based on Network Inference," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 11, no.4: 41-65. http://doi.org/10.4018/IJCINI.2017100103

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Abstract

Mass spectrometry (MS) is an analytical technique for determining the composition of a sample. In bottom-up techniques, peptide mass fingerprinting (PMF) is widely used to identify proteins from MS dataset. In this article, the authors developed a novel network-based inference software termed NBPMF. By analyzing peptide-protein bipartite network, they designed new peptide protein matching score functions. They present two methods: the static one, ProbS, is based on an independent probability framework; and the dynamic one, HeatS, depicts input data as dependent peptides. The authors also use linear regression to adjust the matching score according to the masses of proteins. In addition, they consider the order of retention time to further correct the score function. In post processing, a peak can only be assigned to one peptide in order to reduce random matches. Finally, the authors try to filter out false positive proteins. The experiments on simulated and real data demonstrate that their NBPMF approaches lead to significantly improved performance compared to several state-of-the-art methods.

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