In the diagnosis and treatment of cancer, cancer classification is a vital issue. Gene selection
is much needed to solve the high dimensionality issue in microarray data, small sample size …

The emergence of DNA Microarray technology has enabled researchers to analyze the
expression level of thousands of genes simultaneously. The Microarray data analysis is the …

This systematic review provides researchers interested in feature selection (FS) for
processing microarray data with comprehensive information about the main research …

Microarray gene expression data have become a topic of great interest for cancer
classification and for further research in the field of bioinformatics. Nonetheless, due to the …

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 …

Background Microarray datasets consist of complex and high-dimensional samples and
genes, and generally the number of samples is much smaller than the number of genes …

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 …

Feature selection (FS) is an important area of research in medicine and genetics. Cancer
classification based on the microarray gene expression data is a challenge in this area due …

Background Gene expression data are often used to classify cancer genes. In such high-
dimensional datasets, however, only a few feature genes are closely related to tumors …

Background Microarray data have a high dimension of variables and a small sample size. In
microarray data analyses, two important issues are how to choose genes, which provide …