CRIA: An Interactive Gene Selection Algorithm for Cancers Prediction Based on Copy Number Variations - Wu, Qiang; Li, Dongxi (Frontiers in Plant Science, 2022)
Genomic copy number variations (CNVs) are among the most important structural variations of genes found to be related to the risk of individual cancer and therefore they can be utilized to provide a clue to the research on the formation and progression of cancer. In this paper, an improved computational gene selection algorithm called CRIA (correlation-redundancy and interaction analysis based on gene selection algorithm) is introduced to screen genes that are closely related to cancer from the whole genome based on the value of gene CNVs. The CRIA algorithm mainly consists of two parts. Firstly, the main effect feature is selected that has the greatest correlation with the class label. And then, after the analysis involving correlation, redundancy and interaction, we choose the feature that maximizes the value of the custom selection criterion in each selection round. Based on the real datasets, CRIA selects the top 200 genes to predict the type of cancer. The final experiments’ results of our research show that, compared with the state-of-the-art related methods, the CRIA algorithm can extract the key features of CNVs and a better classification performance can be achieved based on them. In addition, the interpretable genes highly related to cancer can be known, which may provide new clues at the genetic level for the treatment of the cancer.