Previous studies on tumor classification based on feature extraction from gene expression
profiles (GEP) were proven to be effective, but some of such methods lack biomedical …

Background: Tumor classification is one of the most important applications of gene
expression data. Due to high dimensionality in microarray data, dimensionality reduction …

Background Informative gene selection is of great importance in the analysis of microarray
expression data because of its huge dimensionality and relatively small samples, and also …

Gene expression profiles are being used to categorize disease specific genes and classify
different tumor subtypes at the molecular level. Due to the inherent nature of these data …

Microarray gene expression data gained great importance in recent years due to its role in
disease diagnoses and prognoses which help to choose the appropriate treatment plan for …

In bioinformatics research, cancer classification is a crucial domain. The use of microarray
technology to identify specific illnesses is common. A small number of genes uncovered in …

Tumor classification based on Gene Expression Profiles (GEPs), which is of great benefit to
the accurate diagnosis and personalized treatment for different types of tumor, has drawn a …

The classification of different tumor types is of great importance in cancer diagnosis and
drug discovery. However, most previous cancer classification studies are clinical based and …

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 …

The explosion of DNA and protein sequence data in public and private databases has been
encouraging interdisciplinary research on biology and information technology. Gene …