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| 基于支持向量机的拟南芥基因表达数据分析 | |
| 引用本文： | 丁必为,田英杰,邓乃扬,苏震,蔡春.基于支持向量机的拟南芥基因表达数据分析[J].运筹学学报,2006,10(2):51-58. |
| 作者姓名： | 丁必为 田英杰 邓乃扬 苏震 蔡春 |
| 作者单位： | 1. 中国农业大学理学院,北京,100083 2. 中国科学院数据技术与知识经济研究中心,北京,100080 3. 中国农业大学生物学院,北京,100094 |
| 基金项目： | This work is supported by the National Natural Science Foundation of China(No．10371131)． |
| 摘 要： | 针对拟南芥根部基因表达数据分析的问题,本文提出了一种新的基于距离度量学习的支持向机多分类算法．鉴于此问题的特殊性,本文通过最小化4分类机的LOO 误差来求得一个恰当的距离度量．并在此度量下找到若干个属于第5类(其它类)的训练点,从而构造出一个5分类机用来对所有基因分类．实验验证了此算法的可行性,并且比基因表达分析中传统使用的聚类方法更有效． |
| 关 键 词： | 运筹学 支持向量机 距离度量学习 拟南芥 基因表达谱 |
| 收稿时间： | 2006-02-14 |
| 修稿时间： | 2006年2月14日 |
Gene Expression Analysis with Support Vector Machines in Arabidopsis |
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| Ding Biwei,Tian Yingjie,Naiyang Deng,Su Zhen,Cai Chun.Gene Expression Analysis with Support Vector Machines in Arabidopsis[J].OR Transactions,2006,10(2):51-58. | |
| Authors: | Ding Biwei Tian Yingjie Naiyang Deng Su Zhen Cai Chun |
| Abstract: | For the problem of Arabidopsis root gene expression analysis, this paper presents a new algorithm of multi-class Support Vector Machines (SVMs) , which is based on learned distance measure. Because of speciality of this problem, a distance measure is learned by minimizing Leave-one-out (LOO) error of 4-class SVMs, and some genes belong to other classes are determined, then 5-class SVMs is constructed to classify the total genes. Experiments prove the effective of our method compared with traditional clustering methods. |
| Keywords: | Operation research SVMs learning distance metric arabidopsis gene Expression |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |