Informative Gene Selection - An evolutionary approach

Feature selection is one of the major challenges in the analysis of gene expression data, as the number of genes significantly exceeds the number of samples. Principal Component Analysis (PCA), one of the most popular dimensionality reduction techniques, reveals the underlying factors or combinations of original variables without any information loss. This paper studies the application of PCA based on Eigen vectors of covariance and Singular Value Decomposition (SVD) for gene expression dataset as well and explores the problem of feature subset selection, by selecting highly dominating genes to predict cancer at an early stage. The proposed Informative Gene Selection method aims to identify a subset of genes with higher accuracy to represent original genes. Computational time and clustering accuracy is also recorded separately. The proposed method results with more interpretable features that help to identify the target disease quickly. The prominent results show the effectiveness of the proposed algorithm.