Feature Extraction and Analysis of Ovarian Cancer Proteomic Mass Spectra

Abstract

The use of mass spectrometry(MS) as a analytical tool in proteomics is poised to revolutionize early cancer detection and biomarker identification. Although proteomic mass spectra has shown the promising potential of finding disease-related protein patterns, key challenges remain in the processing of them especially for the curse of dimensionality. In the present study, an alternative approach to feature extraction from MS data of ovarian cancer is proposed. The proteomic mass spectrum data after preprocessing are first wrapped into information images that are accordingly mapped to binary images under adaptive threshold. The energy curves of binary images are the result of dimensionality reduction that make up of the alternative biomarker patterns that can be used to classify cancer samples from non-cancer ones using similarity. Applying the procedure to mass spectra of proteomic analysis of serum from ovarian cancer patients and serum from cancer-free individuals in the Food and Drug Administration/National Cancer Institute Clinical Proteomics Database, a sensitivity of 98%, a specificity of 95% and a positive predictive value of 95.15% is obtained.