Gene selection can help the analysis of microarray gene expression data. However, it is very
difficult to obtain a satisfactory classification result by machine learning techniques because …

The classification of cancers from gene expression profiles is a challenging research area in
bioinformatics since the high dimensionality of microarray data results in irrelevant and …

Due to the disproportionate difference between the number of genes and samples,
microarray data analysis is considered an extremely difficult task in sample classification …

One of the key applications of microarray studies is to select and classify gene expression
profiles of cancer and normal subjects. In this study, two hybrid approaches–genetic …

Sample classification is a most critical task in microarray data analysis. But representation of
microarray data with the huge search space of thousands of gene makes this work more …

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

Microarray technology has supplied a large volume of data, which changes many problems
in biology into the problems of computing. As a result techniques for extracting useful …

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 …

Predicting the class of gene expression profiles helps improve the diagnosis and treatment
of diseases. Analysing huge gene expression data otherwise known as microarray data is …

Research has proved that DNA Microarray data containing gene expression profiles are
potentially excellent diagnostic tools in the medical industry. A persistent problem with …