Zhu Wei, Wang Xuena, Ma Yeming, Rao Manlong, Glimm James, Kovach John S
Department of Applied Mathematics and Statistics, State University of New York, Stony Brook, NY 11794, USA.
Proc Natl Acad Sci U S A. 2003 Dec 9;100(25):14666-71. doi: 10.1073/pnas.2532248100. Epub 2003 Dec 1.
We propose a comprehensive pattern recognition procedure that will achieve best discrimination between two or more sets of subjects with data in the same coordinate system. Applying the procedure to MS data 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, we have achieved perfect discrimination (100% sensitivity, 100% specificity) of patients with ovarian cancer, including early-stage disease, from normal controls for two independent sets of data. Our procedure identifies the best subset of proteomic biomarkers for optimal discrimination between the groups and appears to have higher discriminatory power than other methods reported to date. For large-scale screening for diseases of relatively low prevalence such as ovarian cancer, almost perfect specificity and sensitivity of the detection system is critical to avoid unmanageably high numbers of false-positive cases.
我们提出了一种全面的模式识别程序，该程序将在同一坐标系中对两组或多组具有数据的受试者实现最佳区分。将该程序应用于美国食品药品监督管理局/美国国立癌症研究所临床蛋白质组学数据库中卵巢癌患者血清和无癌个体血清的蛋白质组分析质谱数据，对于两组独立数据，我们已实现了卵巢癌患者（包括早期疾病患者）与正常对照之间的完美区分（100%敏感性，100%特异性）。我们的程序确定了用于组间最佳区分的蛋白质组学生物标志物的最佳子集，并且似乎比迄今为止报道的其他方法具有更高的区分能力。对于卵巢癌等相对低患病率疾病的大规模筛查，检测系统几乎完美的特异性和敏感性对于避免出现数量多得难以管理的假阳性病例至关重要。
