Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines.
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.
Peng
Sihua
S
National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, PR China.
Xu
Qianghua
Q
Ling
Xuefeng Bruce
XB
Peng
Xiaoning
X
Du
Wei
W
Chen
Liangbiao
L
D000465:Algorithms; D001483:Base Sequence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D016001:Confidence Intervals; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D006801:Humans; D008969:Molecular Sequence Data; D009369:Neoplasms / Q000145:classification / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*