A new evolutionary gene selection technique

Microarray technology allows to investigate gene expression levels by analyzing high dimensional datasets of few samples. Selection of discriminative, differentially expressed genes from such datasets is important to differentiate, prognose and understand the underlying biological processes. In this regard, the paper presents a new evolutionary gene selection method based on Student-t Stochastic Neighbor Embedding (t-SNE), Differential Evolution (DE) and Support Vector Machine (SVM). Here the underlying classification task of SVM is used as an optimization problem of DE, while t-SNE provides better ordering of genes for selection purpose. Generally, t-SNE is used to reorder the genes in such a way so that similar genes are grouped together and dissimilar genes are kept further apart. These reordered genes are then fragmented into fixed-length partitions. Thereafter, from each partition, a gene is selected randomly to encode the initial population of DE along with the combination of its weight and threshold values in order to participate in fitness computation. In the final generation of DE, a subset of genes is selected based on higher classification accuracy. The proposed technique is tested on six publicly available microarray datasets concerning various cancerous tissues of Homo sapiens and yields a potential set of genes by providing prefect or nearly perfect classification accuracy. Moreover, the superiority of the proposed technique has been demonstrated in comparison with other widely used techniques. Finally, the achieved results have also been justified by a statistical test and allowed us to draw biological conclusions through the identification of Gene Ontologies.