Comparison of supervised classification methods for protein profiling in cancer diagnosis.
A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specific to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spectrometry data using surface enhanced laser desorption/ionization (SELDI) technology. The first two steps are the selection of the most discriminating biomarkers with a construction of different classifiers. Finally, we compare and validate their performance and robustness using different supervised classification methods such as Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Networks, Classification Trees and Boosting Trees. We show that the proposed method is suitable for analysing high-throughput proteomics data and that the combination of logistic regression and Linear Discriminant Analysis outperform other methods tested.
Dossat
Nadège
N
IURC, Department of Biostatistic, Epidemiology and Clinical Research, Montpellier, France. nadege.dossat@iurc.montp.inserm.fr
Mangé
Alain
A
Solassol
Jérôme
J
Jacot
William
W
Lhermitte
Ludovic
L
Maudelonde
Thierry
T
Daurès
Jean-Pierre
JP
Molinari
Nicolas
N