Bioinformatics Advance Access published online on March 15, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti383
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1 University of Tennessee, Department of Statistics, 334 Stokely Management Building, Knoxville, TN 37996-0532
* To whom correspondence should be addressed.
Motivation: Bioinformatics clustering tools are useful at all levels of proteomic data analysis. Proteomics studies can provide a wealth of information and rapidly generate large quantities of data from the analysis of biological specimens. The high dimensionality of data generated from these studies requires the development of improved bioinformatics tools for efficient and accurate data analyses. For proteome profiling of a particular system or organism, a number of specialized software tools are needed. Indeed, significant advances in the informatics and software tools necessary to support the analysis and management of these massive amounts of data are needed. Clustering algorithms based on probabilistic and Bayesian models provide an alternative to heuristic algorithms. The number of clusters (diseased and non diseased groups) is reduced to the choice of the number of components of a mixture of underlying probability. The Bayesian approach is a tool for including information from the data to the analysis. It offers an estimation of the uncertainties of the data and the parameters involved. Results: We present novel algorithms that can organize, cluster and derive meaningful patterns of expression from large scaled proteomics experiments. We processed raw data using a graphical-based algorithm by transforming it from real space data-expression to a complex space data-expression using discrete Fourier transformation; then we used a thresholding approach to denoise and reduce the length of each spectra. Bayesian clustering was applied to the reconstructed data. In comparison with several other algorithms used in this study including K-means, SOM (Kohonen mapping), and LDA (Linear Discriminant Analysis), the Bayesian-Fourier model-based approach displayed superior performance consistently. Selecting the correct model and the number of clusters, thus providing a novel approach for accurate diagnosis of the disease. Using this approach, we were able to successfully denoise proteomic spectra and reach up to a total reduction of the number of peaks compared to the original data. In addition, the Bayesian-based approach generated a better classification rate in comparison with other classification algorithms. This new finding will allow us to apply the Fourier transformation for the selection of the protein profile for each sample, and to develop a novel bioinformatic strategy based on Bayesian clustering for biomarker discovery and optimal diagnosis.
Received January 21, 2004
Revised February 22, 2005
Accepted March 8, 2005
Article
A novel approach for clustering proteomics data using Bayesian Fast Fourier Transform
2 Eastern Virginia Medical School, Department of Microbiology and Molecular Cell Biology, Norfolk, VA 23501
Halima Bensmail, E-mail: bensmail{at}utk.edu
Abstract 
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  B.-J. M. Webb-Robertson and W. R. Cannon Current trends in computational inference from mass spectrometry-based proteomics Brief Bioinform, September 1, 2007; 8(5): 304 - 317. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. J. Wilkinson Bayesian methods in bioinformatics and computational systems biology Brief Bioinform, April 12, 2007; (2007) bbm007v1. [Abstract] [Full Text] [PDF]
|
|