Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines

被引：168

作者：

Peng, SH

Xu, QH

Ling, XB

Peng, XN

Du, W

Chen, LB ^{[1
]}

机构：

[1] Zhejiang Univ, Coll Life Sci, Hangzhou 310029, Peoples R China

[2] Zhejiang Univ, Natl Lab Ind Control Technol, Hangzhou 310027, Peoples R China

[3] Tularik Inc, San Francisco, CA 94080 USA

[4] Univ Texas, MD Anderson Canc Ctr, Dept Mol Genet, Houston, TX 77030 USA

[5] Chinese Acad Sci, Inst Genet & Dev Biol, Beijing 100101, Peoples R China

来源：

FEBS LETTERS | 2003年 / 555卷 / 02期

关键词：

microarray; support vector machine; genetic algorithm; recursive feature elimination; cancer;

D O I：

10.1016/S0014-5793(03)01275-4

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Simultaneous multiclass classification of tumor types is essential for future clinical implementations of microarray-based cancer diagnosis. In this study, we have combined genetic algorithms (GAs) and all paired support vector machines (SVMs) for multiclass cancer identification. The predictive features have been selected through iterative SVMs/GAs, and recursive feature elimination post-processing steps, leading to a very compact cancer-related predictive gene set. Leave-one-out cross-validations yielded accuracies of 87.93% for the eight-class and 85.19% for the fourteen-class cancer classifications, outperforming the results derived from previously published methods. (C) 2003 Published by Elsevier B.V. on behalf of the Federation of European Biochemical Societies.

引用

页码：358 / 362

页数：5