Cancer diagnosis is an important emerging clinical application of microarray data. Its
accurate prediction to the type or size of tumors relies on adopting powerful and reliable …

Nowadays, microarray gene expression data plays a vital role in tumor classification.
However, due to the accessibility of a limited number of tissues compared to large number of …

Selecting relevant and discriminative genes for sample classification is a common and
critical task in gene expression analysis (eg disease diagnostic). It is desirable that gene …

The rapid development of DNA microarray technology enables researchers to measure the
expression levels of thousands of genes simultaneously and allows biologists easily gain …

Background Microarray datasets are an important medical diagnostic tool as they represent
the states of a cell at the molecular level. Available microarray datasets for classifying cancer …

Recently, more and more machine learning techniques have been applied to microarray
data analysis. The aim of this study is to propose a genetic programming (GP) based new …

In this paper, we present a gene selection method based on genetic algorithm (GA) and
support vector machines (SVM) for cancer classification. First, the Wilcoxon rank sum test is …

DNA microarray data is expected to be a great help in the development of efficient diagnosis
and tumor classification. However, due to the small number of instances compared to a large …

Background Microarray experiments are becoming a powerful tool for clinical diagnosis, as
they have the potential to discover gene expression patterns that are characteristic for a …

It is essential to select a minimal number of relevant genes from microarray data while
maximizing classification accuracy for the development of inexpensive diagnostic tests …