Systematic identification of multiple tumor types in microarray data based on hybrid differential evolution algorithm

Chun Liang Lu*, Tsan Cheng Su, Tsun Chen Lin, I. Fang Chung

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish
Pages (from-to)S237-S244
JournalTechnology and Health Care
Volume24
Issue numbers1
DOIs
StatePublished - 8 Dec 2015

Keywords

  • cancer classification
  • Gene selection
  • hybrid differential evolution
  • silhouette statistics

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