Diagnosis of Prostate Cancer by Wavelet Based Feature Extraction Method Using Blood Proteomic Patterns

Pathologic states within the prostate may be reflected by changes in serum proteomic patterns. Mass spectrometry is becoming an important tool that generates the proteomic patterns. Mass spectrometry yields complex functional data for which the features of scientific interest are the peaks. Due to this complexity of data, a higher order analysis such as wavelet transform is needed to uncover the differences in proteomic patterns. We have applied wavelet based feature extraction method to available data and used a filter approach to feature subset selection in order to identify the appropriate biomarkers from reconstructed mass spectra. Using different classification algorithms, our approach yielded an accuracy of 95%, specificity of 95%, and sensitivity of 96%.

کلیدواژه ها:

Proteomics ، Cancer diagnosis ، Wavelet transform ، Classification ، Biomarker