Wang Xuena, Zhu Wei, Pradhan Kith, Ji Chen, Ma Yeming, Semmes Oliver John, Glimm James, Mitchell Joseph
State University of New York, Stony Brook, NY, USA.
Proteomics. 2006 Apr;6(7):2095-100. doi: 10.1002/pmic.200500459.
Feature extraction or biomarker selection is a critical step in disease diagnosis and knowledge discovery based on protein MS. Many studies have discussed the classification methods applied in proteomics; however, few could be found to address feature extraction in detail. In this paper, we developed a systematic approach for the extraction of mass spectrum peak apex and peak area with special emphasis on noise filtration and peak calibration. Application to a head and neck cancer data generated at the Eastern Virginia Medical School [Wadsworth, J. T., Somers, K. D., Cazares, L. H., Malik, G. et al.., Clin. Cancer Res. 2004, 10, 1625-1632] revealed that the new feature extraction method would yield consistent and highly discriminatory biomarkers.
特征提取或生物标志物选择是基于蛋白质质谱的疾病诊断和知识发现中的关键步骤。许多研究讨论了蛋白质组学中应用的分类方法;然而,很少有研究能详细探讨特征提取。在本文中,我们开发了一种系统方法来提取质谱峰顶点和峰面积,特别强调噪声过滤和峰校准。将其应用于东弗吉尼亚医学院生成的头颈癌数据[Wadsworth, J. T., Somers, K. D., Cazares, L. H., Malik, G.等人,《临床癌症研究》2004年,10卷,1625 - 1632页]表明,这种新的特征提取方法将产生一致且具有高度区分性的生物标志物。