Detecting biomarkers from microarray data using distributed correlation based gene selection

Over the past few decades, DNA microarray technology has emerged as a prevailing process for early identification of cancer subtypes. Several feature selection (FS) techniques have been widely applied for identifying cancer from microarray gene data but only very few studies have been conducted on distributing the feature selection process for detecting cancer subtypes.

Not all the gene expressions are needed in prediction, this research article objective is to select discriminative biomarkers by using distributed FS method which helps in accurately diagnosis of cancer subtype. Traditional feature selection techniques have several drawbacks like unrelated features that could perform well in terms of classification accuracy with a suitable subset of genes will be left out of the selection.

To overcome the issue, in this paper a new filter-based method for gene selection is introduced which can select the highly relevant genes for distinguishing tissues from the gene expression dataset. In addition, it is used to compute the relation between gene-gene and gene-class and simultaneously identify subset of essential genes. Our method is tested on Diffuse Large B cell Lymphoma (DLBCL) dataset by using well-known classification techniques such as support vector machine, naïve Bayes, k-nearest neighbor, and decision tree.

Results on biological DLBCL dataset demonstrate that the proposed method provides promising tools for the prediction of cancer type, with the prediction accuracy of 97.62%, precision of 94.23%, sensitivity of 94.12%, F-measure of 90.12%, and ROC value of 99.75%.

The experimental results reveal the fact that the proposed method is significantly improved classification accuracy and execution time, compared to existing standard algorithms when applied to the non-partitioned dataset. Furthermore, the extracted genes are biologically sound and agree with the outcome of relevant biomedical studies.