Paper Info
Title
Towards the Prediction of Protein Abundance from Tandem Mass Spectrometry Data
Abstract
This paper addresses a central problem of Proteomics: estimating the amounts of each of the thousands of proteins in a cell culture or tissue sample. Although laboratory methods involving isotopes have been developed for this problem, we seek a method that uses simpler laboratory procedures. Specifically, our aim is to use data-mining techniques to infer protein levels from the relatively cheap and abundant data available from high-throughput tandem mass spectrometry (MS/MS). We have developed and evaluated several techniques for tackling this problem, including the development of three generative models of MS/MS data, and methods for efficiently fitting the models to data. in addition, we tested each method on three real-world datasets generated by MS/MS experiments performed on various tissue samples taken from Mouse. This paper outlines the biological problem and presents a selection of our results.
Year
Venue
Keywords
2006
SIAM PROCEEDINGS SERIES
proteomics,tandem mass spectrometry,data mining,cell culture,bioinformatics,proteins,high throughput
Field
DocType
Citations 
Proteomics,Pattern recognition,Computer science,Tandem mass spectrometry,Artificial intelligence,Biological Problem
Conference
2
PageRank 
References 
Authors
7.79
2
2
Name
Order
Citations
PageRank
Anthony J. Bonner1733422.63
Han Liu248.68