Cancer Classification via Machine Learning Based on Genomic Data

The human genome contains valuable information about the person and its hand analysis is cumbersome since it consists of billions of nucleotides. This work computationally analyses the mutations in the human genome which are the main source of differences and diseases. It utilizes ensembling of various supervised machine learning models on a cancer dataset that is obtained from TCGA and labels the genomes with their cancer types at 74% test accuracy. The obtained accuracies outperform the works in the literature such as DeepGene and create promising results for the future study.