An evolutionary approach for gene expression patterns.
This study presents an evolutionary algorithm, called a heterogeneous selection genetic algorithm (HeSGA), for analyzing the patterns of gene expression on microarray data. Microarray technologies have provided the means to monitor the expression levels of a large number of genes simultaneously. Gene clustering and gene ordering are important in analyzing a large body of microarray expression data. The proposed method simultaneously solves gene clustering and gene-ordering problems by integrating global and local search mechanisms. Clustering and ordering information is used to identify functionally related genes and to infer genetic networks from immense microarray expression data. HeSGA was tested on eight test microarray datasets, ranging in size from 147 to 6221 genes. The experimental clustering and visual results indicate that HeSGA not only ordered genes smoothly but also grouped genes with similar gene expressions. Visualized results and a new scoring function that references predefined functional categories were employed to confirm the biological interpretations of results yielded using HeSGA and other methods. These results indicate that HeSGA has potential in analyzing gene expression patterns.
Tsai
Huai-Kuang
HK
Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, ROC. d7526010@csie.ntu.edu.tw
Yang
Jinn-Moon
JM
Tsai
Yuan-Fang
YF
Kao
Cheng-Yan
CY
D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D004252:DNA Mutational Analysis / Q000379:methods*; D019143:Evolution, Molecular; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000235:genetics*; D008040:Genetic Linkage / Q000235:genetics; D014644:Genetic Variation; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods*