Selecting a minimal number of relevant genes from microarray data to design accurate tissue classifiers.
It is essential to select a minimal number of relevant genes from microarray data while maximizing classification accuracy for the development of inexpensive diagnostic tests. However, it is intractable to simultaneously optimize gene selection and classification accuracy that is a large parameter optimization problem. We propose an efficient evolutionary approach to gene selection from microarray data which can be combined with the optimal design of various multiclass classifiers. The proposed method (named GeneSelect) consists of three parts which are fully cooperated: an efficient encoding scheme of candidate solutions, a generalized fitness function, and an intelligent genetic algorithm (IGA). An existing hybrid approach based on genetic algorithm and maximum likelihood classification (GA/MLHD) is proposed to select a small number of relevant genes for accurate classification of samples. To evaluate the performance of GeneSelect, the gene selection is combined with the same maximum likelihood classification (named IGA/MLHD) for convenient comparisons. The performance of IGA/MLHD is applied to 11 cancer-related human gene expression datasets. The simulation results show that IGA/MLHD is superior to GA/MLHD in terms of the number of selected genes, classification accuracy, and robustness of selected genes and accuracy.
Huang
Hui-Ling
HL
Department of Information Management, Jin-Wen Institute of Technology, Hsin-Tien 231, Taiwan.
Lee
Chong-Cheng
CC
Ho
Shinn-Ying
SY
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D005821:Genetic Techniques; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D049490:Systems Biology; D018511:Systems Integration; D014018:Tissue Distribution