Title :
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
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;
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
DOI :
10.1109/DSPWS.2006.265485
