乳腺癌代谢物组模式特征发现方法及HPLC/MS/MS分析

提出一种基于单独最优特征组合和BP神经网络的代谢物组模式特征发现方法，并用其寻找到尿样中与乳腺癌最为相关的4种核苷，组成一组特异性检测参数，经HPLC／MS／MS联用法鉴定，它们是乳清酸核苷、1-甲酰化腺苷、S-腺苷-L-蛋氨酸及，N^2-甲酰化鸟苷，将这4种核苷作为输入变量，用BP神经分类网络建立乳腺癌诊断模型，留一法交叉验证和独立验证结果表明，该模型预测准确率达到90％以上。

Pattern Feature Discovery for Metabonomics of Breast Cancer and HPLC/MS/MS Analysis of Characteristic Metabolites

A new pattern discovery method based on the best individual feature selection and BP neural network was proposed to select characteristic metabolites in urine which were most correlative with breast cancer.Four nucleosides(orotidine,1-methyladenosine,S-adenosylmethionine,and N~2-methylguanosine),which were identified by using HPLC/MS/MS,were selected out and composed a characteristic pattern for diagnosis of breast cancer.Subsequently,BP neural network was investigated as potential tools to diagnose breast cancer by using those four nucleosides as the input features.The results of Leave-One-Out and independent cross validation show that the prediction rate of the model built with BP neural network is higher than 90%.As a consequence,those four selected nucleosides could be considered as a characteristic pattern for the diagnosis of breast cancer.