Affiliations: [a] Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan | [b] Department of Applied Information and Multimedia, Ching Kuo Institute of Management and Health, Keelung County, Taiwan | [c] Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien County, Taiwan | [d] Department of Computer and Communication Engineering, Dahan Institute of Technology, Hualien County, Taiwan
Correspondence:
[*]
Corresponding author: Chun-Liang Lu, 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. E-mail:[email protected]
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.
Keywords: Gene selection, cancer classification, hybrid differential evolution, silhouette statistics
