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

*此作品的通信作者

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)S237-S244
期刊Technology and Health Care
24
發行號s1
DOIs
出版狀態Published - 8 12月 2015

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