title: Systematic identification of multiple tumor types in microarray data based on hybrid differential evolution algorithm.
abstract: Correct classification and prediction of tumor cells are essential for microarrays to construct a diagnostic system. Differential evolution (DE) is a powerful optimization algorithm, which has been widely used in many areas. However, the standard DE and most of its variants search in the continuous space, which cannot solve the binary optimizations directly. In this paper, the hybrid framework based on the binary DE algorithm and silhouette filter, is proposed to improve searching ability to classify breast and leukemia cancers in microarray for biomarker discovery. The study is focused to use hybrid DE algorithm for gene selection and silhouette statistics as a discriminant function to classify multiple tumor types in microarray data. Distance metrics on silhouette statistics have also been discussed for high classification accuracy. Experimental results show that the hybrid method is effective to discriminate breast and leukemia cancer subtypes and find potential biomarkers for cancer diagnosis.
authors.affiliation: Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.|Department of Applied Information and Multimedia, Ching Kuo Institute of Management and Health, Keelung County, Taiwan.
authors.forename: Chun-Liang
authors.initials: CL
authors.lastname: Lu
authors.affiliation: Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien County, Taiwan.
authors.forename: Tsan-Cheng
authors.initials: TC
authors.lastname: Su
authors.affiliation: Department of Computer and Communication Engineering, Dahan Institute of Technology, Hualien County, Taiwan.
authors.forename: Tsun-Chen
authors.initials: TC
authors.lastname: Lin
authors.affiliation: Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.
authors.forename: I-Fang
authors.initials: IF
authors.lastname: Chung
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000235:genetics; D015203:Reproducibility of Results
keywords: Gene selection; cancer classification; hybrid differential evolution; silhouette statistics