An Approximate Bayesian Detection Scheme with Applications to Tandem Mass Spectrometry Data Analysis

Author

Emanuele, Vincent A., II ; Olman, Victor ; Yan, Bo ; Xu, Ying ; Zhou, G. Tong

Author_Institution

Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA

fYear

2006

fDate

24-27 Sept. 2006

Firstpage

555

Lastpage

560

Abstract

In this paper, we present a solution to a special classification problem that we have encountered during the analysis of tandem mass spectrometry data of proteins. First, we present a thorough statistical analysis of our data set. From this analysis, we build a model for the data that allows us to formulate our mass spectrometry data analysis problem as a special kind of classification problem. We propose a solution to this problem and show some results on simulated and real data sets

Keywords

Bayes methods; biochemistry; biological techniques; mass spectra; molecular biophysics; proteins; Bayesian detection scheme; mass spectrometry data analysis; proteins; simulated data sets; statistical analysis; Application software; Bayesian methods; Biochemistry; Biology computing; Chemicals; Costs; Data analysis; Data engineering; Mass spectroscopy; Proteins; Bayes detection theory; classification; mass spectrometry;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Signal Processing Workshop, 12th - Signal Processing Education Workshop, 4th

Conference_Location

Teton National Park, WY

Print_ISBN

1-4244-3534-3

Electronic_ISBN

1-4244-0535-1

Type

conf

DOI

10.1109/DSPWS.2006.265485

Filename

4041126