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Accueil du site > Séminaires > Probabilités Statistiques et réseaux de neurones > Biomarker discovery in MALDI-TOF serum protein profiles using discrete wavelet transformation

Vendredi 19 décembre 2008 à 11h00

Biomarker discovery in MALDI-TOF serum protein profiles using discrete wavelet transformation

Theodore Alexandrov (Université de Breme, Allemagne)

Résumé : Automatic classification of high-resolution mass spectrometry proteomic data has increasing potential in the early diagnosis of cancer. We present and discuss a new procedure of biomarker discovery in serum protein profiles based on :

(1) discrete wavelet transformation of the spectra,

(2) selection of discriminative wavelet coefficients by a statistical test,

(3) building and evaluating a support vector machine classifier by double cross-validation with attention to the generalizability of the results.

In addition to the evaluation results (total recognition rate, sensitivity and specificity), the procedure provides the biomarker patterns, i.e. the parts of spectra which discriminate cancer and control individuals. The evaluation was performed on MALDI-TOF serum protein profiles of 66 colorectal cancer patients and 50 controls. Our procedure provided a high recognition rate (97.3%), sensitivity (98.4%), and specificity (95.8%). The extracted biomarker patterns mostly represent the peaks expressing mean differences between the cancer and control spectra. However, we show that the discriminative power of a peak is not simply expressed by its mean height and can not be derived by comparison of the mean spectra. The obtained classifiers have high generalization power as measured by the number of support vectors. This prevents overfitting and contributes to the reproducibility of the results, which is required to find biomarkers differentiating cancer patients from healthy individuals.

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