Microarray Survival Analysis of Five Cancer Gene Sequences and its Application using Bayesian Additive Regression Trees (BART)

An important tool in the Genomic approach is the widely used Microarray interventions to effectively and accurately predict survival time and risk analysis of patients. This study is aimed to present and examine the risk impact of five cancer gene sequences with different high-dimensional microarray data sets, through survival analysis. GSE10300; GSE14333; GSE16446; GSE17618 and GSE20685 data sets with 16,183; 54,712; 54,739; 54,726 and 54,739 gene counts from sample sizes 44, 226, 107, 44 and 327 respectively were selected from the NCBI database to identify their risk impact. Then the classical Cox Proportional Hazard (CoxPH), Bayesian Adaptive Spline Surface (BASS) and Bayesian Additive Regression Tree (BART) models were employed to analyze each of the five data sets. Each model’s prediction performance was measured using the Root Mean Squared Error (RSME). The fol-lowing, R packages ‘survive’ for CoxPH; ‘BASS’ for BASS and ‘bartMachine’ for BART were utilized in obtaining the results. Finally, influential variable plots were shown to indicate the most important main effects and interactions for the five microarray datasets. Varying median hazard values and traces of yellow boxes across the diagonals of the heatmap diagrams indicate multi-collinearity between genes, implying misleading results by the CoxPH model, while leveraging superiority of BART model over the BASS and CoxPH models. The RSME plots successfully identify some influential genes among thou-sands of genes across the five real-life microarray datasets analyzed: CoxPH model shows a moderate level of predictive accuracy while the BASS model exhibits both strengths and weaknesses depending on the number of predictors used, significantly higher than CoxPH. The BART model, on the other hand, with the lowest RSME, consistently demonstrates its superior, stable and consistent predictive performance over CoxPH and BASS models. This study gives a more distinct and nuanced understanding of genes interactions and influence, as well as provide improved prognostic tools and personalized treatment strategies for cancer patients’ risks and survival.