A Proposed Ensemble Classifier (EC) Machine Learning Algorithm for Evaluation of Plausible Drivers of Metastatic Breast Cancer

Today, there is a lot of markers on the prognosis and diagnosis of complex diseases such as cancer. However, our understanding of the drivers that influence cancer aggression is limited. Meanwhile, the exploration of the targeted gene panel for cancer development, with the help and power of machine learning methods, has ushered in a new era of high-throughput data analysis, where it is difficult for human physicians and biologists to make decisions on the basis of clinical practice or laboratory evidence. Present study aims to focus on the synergistic combination of computational tools as an integrative approach to design plausible driver genes for metastatic breast cancer (MBCA). It can open new horizons for assisting precision oncologists to screen patients at risk and eliminate the need for whole-genome/exome sequencing.