MALDI-MS data analysis for disease biomarker discovery.

In this chapter, we address the issue of matrix-assisted laser desorption/ionization mass spectrometry (MS) data analysis for disease biomarker discovery. We first give a general framework of MS data analysis, then focus on several key steps. After that, we show some application examples using an ovarian sera cancer dataset. Finally, we discuss the limitations of current approaches and possible future research directions.

[1]  Robert Tibshirani,et al.  Sample classification from protein mass spectrometry, by 'peak probability contrasts' , 2004, Bioinform..

[2]  Liang Chen,et al.  A statistical method for identifying differential gene-gene co-expression patterns , 2004, Bioinform..

[3]  P. Eilers Parametric time warping. , 2004, Analytical chemistry.

[4]  Somnath Datta,et al.  Standardization and denoising algorithms for mass spectra to classify whole-organism bacterial specimens , 2004, Bioinform..

[5]  Hongyu Zhao,et al.  Detecting and aligning peaks in mass spectrometry data with applications to MALDI , 2006, Comput. Biol. Chem..

[6]  S. Dudoit,et al.  STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS , 2002 .

[7]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[8]  David Ward,et al.  Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data , 2003, Bioinform..

[9]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  R D Appel,et al.  Improving protein identification from peptide mass fingerprinting through a parameterized multi‐level scoring algorithm and an optimized peak detection , 1999, Electrophoresis.

[11]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[12]  Edmond J. Breen,et al.  Automatic Poisson peak harvesting for high throughput protein identification , 2000, Electrophoresis.

[13]  George M. Church,et al.  Aligning gene expression time series with time warping algorithms , 2001, Bioinform..

[14]  P. Schellhammer,et al.  Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. , 2002, Clinical chemistry.

[15]  Jeffrey S. Morris,et al.  Improved peak detection and quantification of mass spectrometry data acquired from surface‐enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform , 2005, Proteomics.

[16]  Y. Yasui,et al.  An Automated Peak Identification/Calibration Procedure for High-Dimensional Protein Measures From Mass Spectrometers , 2003, Journal of biomedicine & biotechnology.

[17]  J. Carstensen,et al.  Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping , 1998 .

[18]  Hans Knutsson,et al.  Signal processing for computer vision , 1994 .

[19]  T W Randolph,et al.  Multiscale Processing of Mass Spectrometry Data , 2006, Biometrics.

[20]  Jeffrey S. Morris,et al.  Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization. , 2003, Clinical chemistry.

[21]  B. W. Wright,et al.  High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis. , 2003, Journal of chromatography. A.

[22]  Ralf J. O. Torgrip,et al.  Peak alignment using reduced set mapping , 2003 .

[23]  E. Petricoin,et al.  Use of proteomic patterns in serum to identify ovarian Cancer , 2002 .

[24]  J. Potter,et al.  A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection. , 2003, Biostatistics.