Two-Stage Gene Selection Tactic For Identifying Significant Prognosis Biomarkers In Breast Cancer

Abstract

The mining of a subset of informative genes from microarray gene expression data is a significant data preparation task in the classification of breast cancer. Out of all the algorithms developed, CFS-BFS and CONSISTENCY-BFS are the two best ones for gene selection. For reliable prognostication of breast cancer subtypes, a ground-breaking 2-Stage Gene Selection algorithm has been developed. Using CFS-BFS in the first stage and CONSISTENCY-BFS in the second, the majority of the distracting, inappropriate, and redundant genes are removed. To improve algorithm efficacy, the 2-Stage GeS strategy gets around the uncertainty problem with CFS-BFS. Surprisingly, using Hidden Weight Naive Bayes to establish the 2-Stage GeS, more accurate and reliable results are obtained. The standings of recall, precision, f-score, and fallout show encouraging results. The top four genes E2F3, PSMC3IP, GINS1 and PLAGL2 were further verified by applying Kaplan-Meier Survival Model. E2F3 and GINS1 are likely targets for precision therapy