title: Mono-isotope Prediction for Mass Spectra Using Bayes Network.
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.
authors.affiliation: Department of Systems and Computer Science, Howard University, Washington, DC 20059, USA.
authors.forename: Hui
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Systems and Computer Science, Howard University, Washington, DC 20059, USA.
authors.forename: Chunmei
authors.initials: C
authors.lastname: Liu
authors.affiliation: Department of Systems and Computer Science, Howard University, Washington, DC 20059, USA.
authors.forename: Mugizi Robert
authors.initials: MR
authors.lastname: Rwebangira
authors.affiliation: Department of Systems and Computer Science, Howard University, Washington, DC 20059, USA.
authors.forename: Legand
authors.initials: L
authors.lastname: Burge
keywords: Bayes network; mono-isotope prediction; tandem mass spectrum