… into the problem of cancer classification, systematic approaches based on global gene
expression analysis have been proposed. The expression level of genes is known to contain the …

… gene expression data allow quantifying the expression levels of genes and produce valuable
data for computational analysis. … in gene expression analysis for cancer classification using …

… learning based gene expression data analysis with different feature selection algorithms. …
classification of large data set like gene expression. Therefore proper cancer classification can …

… The objective of this study is to investigate the performance of ensemble machine learning
in classifying gene expression data on cancer classification problems. This paper outlines the …

… In this paper, we have proposed a hybrid CAD method of cancer classification taking the
advantage of both filter and wrapper methods. A fast dimensionality reduction step is carried out …

… Cancer classification based on gene expression profiles has provided insight on the causes
of cancer and cancer … downstream cancer analysis to address the large differences in gene …

… cancer classification, which has received increasing attention in bioinformatics and computational
biology. The development of cancer classification … to the cancer classification problem, …

… precise classification models. This paper proposes a new classification technique for gene
expression data… The key idea is to employ robust neighbors from training data by using a new …

… level, it is challenging to work with RNA-Seq data due to their spatial features. Eight DL …
study for cancer classification from gene expression data. In this study, we use RNA-Seq data of …

… In this review, we assess the conceptual and practical contribution of gene expression
profiling in breast cancer, with special emphasis on assays currently used in clinical practice or …