Feature Eztraction and Analysis of Ovarian Cancer Proteomic Mass Spectra
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.
Hui Meng Wenxue Hong Jialin Song Liqiang Wang
College of Electrical Engineering Yanshan University Qinhuangdao, Hebei Province, China College of Automotive and Energy Engineering Yanshan University Qinhuangdao, Hebei Province, China
国际会议
上海
英文
668-671
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）