| 核方法在SELDI-TOF蛋白质组学数据分类中的应用 |
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| 引用本文： | 唐凯临,李通化,陈开,黄金艳.核方法在SELDI-TOF蛋白质组学数据分类中的应用[J].计算机与应用化学,2007,24(1):107-109. |
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| 作者姓名： | 唐凯临 李通化 陈开 黄金艳 |
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| 作者单位： | 同济大学大学化学系,上海市四平路1239号,200092 |
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| 基金项目： | 国家自然科学基金(20275026)~~ |
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| 摘 要： | 高解析质谱目前应用于疾病分类和治疗,然而海量数据的分析却遇到相当大的挑战。本文使用结合预处理的Kernel-PLS方法,可用于SELDI-TOF蛋白质组学数据分类。留一法交叉验证得到了敏感性0．9833和特异性1．0000的结果。
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| 关 键 词： | SELDI-TOF Kernel-PLS 分类 |
| 文章编号： | 1001-4160（2007）00-107-109 |
| 修稿时间： | 2006-10-302006-12-28 |
Application of kernel method to classifiy SELDI-TOF proteomics data |
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| Tang Kailin,Li Tonghua,Chen Kai,Huang Jinyan.Application of kernel method to classifiy SELDI-TOF proteomics data[J].Computers and Applied Chemistry,2007,24(1):107-109. |
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| Authors: | Tang Kailin Li Tonghua Chen Kai Huang Jinyan |
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| Affiliation: | Department of Chemistry, Tongji University, Shanghai, 200092, China |
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| Abstract: | High-resolution mass spectrometry instruments are increasingly used for disease classification and therapeutic guidance. However, the analysis of immense amount of data poses considerable challenges. We show that the Kernel-PLS method, combining with pretreatment, is capable of classifying SELDI-TOF proteomics data. The method achieves an average sensitivity of 0.9833 and an average specificity of 1.0000 in leave-one-out cross-validations. |
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| Keywords: | SELDI-TOF Kernel-PLS classification |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
