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

FEBS Letters

Chen, Liangbiao¹  Xu, Qianghua¹  Peng, Xiaoning³  Ling, Xuefeng Bruce⁴  Du, Wei²  Peng, Sihua² 

[1] College of Life Sciences, Zhejiang University, Hangzhou 310029, PR China;National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, PR China;Department of Molecular Genetics, MD Anderson Cancer Center, University of Texas, Houston, TX 77030, USA;Tularik Inc., South San Francisco, CA 94080, USA

关键词: Microarray;    Support vector machine;    Genetic algorithm;    Recursive feature elimination;    Cancer;    LOOCV;    leave-one-out cross-validation;    GA;    genetic algorithm;    SVM;    support vector machine;    RFE;    recursive feature elimination;    AP;    all paired;   

DOI  :  10.1016/S0014-5793(03)01275-4

学科分类：生物化学/生物物理

来源: John Wiley & Sons Ltd.

【 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.

【 授权许可】

Unknown