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| 由质谱分析数据建立乙肝病人与健康人的分类模型 | |
| 引用本文： | 徐琨,朱尔一,杨芃原,刘银坤.由质谱分析数据建立乙肝病人与健康人的分类模型[J].分析化学,2009,37(2). |
| 作者姓名： | 徐琨 朱尔一 杨芃原 刘银坤 |
| 作者单位： | 1. 厦门大学化学化工学院现代分析科学教育部重点实验室,厦门,361005 2. 复旦大学化学系,上海,200433;复旦大学生物医学研究院,上海,200032 3. 复旦大学附属中山医院肝癌研究所,上海,200032;复旦大学生物医学研究院,上海,200032 |
| 摘 要： | 建立运用表面加强激光解吸电离-飞行时间质谱获取乙肝病人和健康人血清蛋白指纹图谱数据,并用偏最小二乘(PLS)变量筛选法建立乙型肝炎(HBV)病人和健康人的分类模型,最终得到分类模型的交叉检验相关系数达0.97以上,判别准确率显著提高.对模型进行分析,找出对乙型肝炎病人和健康人的差异有重要影响的因素或变量.这些变量为某些质荷比区间内特定蛋白的峰强度值,反映这些质荷比区间内蛋白量的增加或减少,与乙肝病的形成有密切关系,可作为重要的生物标志物,进一步加以研究.本研究采用所得模型的拟合值等一些信息来做分类图,能较好地表达回归模型的分类效果. |
| 关 键 词： | 乙型肝炎 表面加强激光解吸电离-飞行时间质谱 偏最小二乘变量筛选 分类模型 海量数据建模 |
Classification Model of Hepatitis B Virus Patients and Healthic People Established from Mass Spectrometric Data |
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| XU Kun,ZHU Er-Yi,YANG Peng-Yuan,LIU Yin-Kun.Classification Model of Hepatitis B Virus Patients and Healthic People Established from Mass Spectrometric Data[J].Chinese Journal of Analytical Chemistry,2009,37(2). | |
| Authors: | XU Kun ZHU Er-Yi YANG Peng-Yuan LIU Yin-Kun |
| Institution: | Department of Chemistry and the Key Laboratory of Analytical Sciences of the Ministry of Education;College of Chemistry and Chemical Engineering;Xiamen University;Xiamen 361005;Department of Chemistry;Fudan University;Shanghai 200433;Liver Cancer Institute;Zhongshan Hospital;Shanghai 200032;Institute of Biomedical Sciences;Shanghai 200032 |
| Abstract: | Classification models of hepatitis B virus(HBV)patients and healthy people have been built from mass spectrometric data,which can be used for the detection of HBV.Surface-enhanced laser desorption and ionization-time of flight-mass spectrometry(SELDI-TOF-MS)technique has been applied to gain the data of serum protein from two kinds of people and then PLS variable selection method has been used to establish the classification model.The cross validation relativities coefficients of the model came to 0.97.Furt... |
| Keywords: | Hepatitis B virus surface-enhanced laser desorption and ionization-time of flight-mass spectrometry partial least squares variable selection classification model huge data modeling |
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