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Open Access

Mono-isotope Prediction for Mass Spectra Using Bayes Network

Department of Systems and Computer Science, Howard University, Washington, DC 20059, USA.
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Abstract

Mass spectrometry is one of the widely utilized important methods to study protein functions and components. The challenge of mono-isotope pattern recognition from large scale protein mass spectral data needs computational algorithms and tools to speed up the analysis and improve the analytic results. We utilized naïve Bayes network as the classifier with the assumption that the selected features are independent to predict mono-isotope pattern from mass spectrometry. Mono-isotopes detected from validated theoretical spectra were used as prior information in the Bayes method. Three main features extracted from the dataset were employed as independent variables in our model. The application of the proposed algorithm to publicMo dataset demonstrates that our naïve Bayes classifier is advantageous over existing methods in both accuracy and sensitivity.

Keywords

Bayes networktandem mass spectrummono-isotope prediction

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Tsinghua Science and Technology
Volume 19 Issue 6,
December 2014
Pages 617-623
DOI: 10.1109/TST.2014.6961030
Cite this article:
Li H, Liu C, Rwebangira MR, et al. Mono-isotope Prediction for Mass Spectra Using Bayes Network. Tsinghua Science and Technology, 2014, 19(6): 617-623. https://doi.org/10.1109/TST.2014.6961030

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Received: 18 May 2014
Revised: 09 June 2014
Accepted: 16 June 2014
Published: 20 November 2014
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