Background Selecting a parsimonious set of informative genes to build highly generalized
performance classifier is the most important task for the analysis of tumor microarray …

Background/Objectives: Accurate classification in cancer research is vital for devising
effective treatment strategies. Precise cancer classification depends significantly on …

In efforts to discover disease mechanisms and improve clinical diagnosis of tumors, it is
useful to mine profiles for informative genes with definite biological meanings and to build …

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 …

Background Previous studies on tumor classification based on gene expression profiles
suggest that gene selection plays a key role in improving the classification performance …

For cancer classification problems based on gene expression, the data usually has only a
few dozen sizes but has thousands to tens of thousands of genes which could contain a …

Over the past few decades, there has been a massive growth in the volume of biological
data. In such datasets, the influence of dimensionality bias or existence of repetitive, noisy …

Background Even though the classification of cancer tissue samples based on gene
expression data has advanced considerably in recent years, it faces great challenges to …

Background As many complex omics data have been generated during the last two
decades, dimensionality reduction problem has been a challenging issue in better mining …

Simple Summary Here we introduce a new feature selection algorithm DTA, which selects
important, non-redundant, and relevant features from diverse omics data. DTA selects non …