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.

An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data

“…At present, the treatment of breast cancer is seriously lagging behind. Although a number of effective measures have been identified, it is hoped to identify the tumor and prepare for further diagnosis [ 9 – 14 ]. However, as a late intervention method, the effect is still limited.…”

“…However, as a late intervention method, the effect is still limited. The generation and development of tumor are closely related to genes, and, from gene level to cancer diagnosis, can also be detected by the gene [ 14 – 18 ]. For example, Karlsson A et al [ 19 ] applied unsupervised analysis of gene expression data and identified a phenotype comprising 90% of 2015 world health organization (WHO) lung cancer.…”