Abstract
A novel MLAC (Machine Learning Against Cancer) method to distinguish between cancerous and noncancerous RNA genomic data is developed and tested with 100\% accuracy on all healthy and cancerous Breast tissue samples. A Naive Bayesian ML (Machine Learning) system is trained using WES (Whole Exome Sequencing) data in a high-level i.e. normalized quantification of RNAs obtained from 1091 breast cancer samples’ WES files from the TCGA (The Cancer Genome Atlas) and 179 healthy samples’ WES data from the GTEx (Genotype-Tissue Expression) project. We could show that both sensitivity and specificity of the method in classification of cancerous and noncancerous cells is perfectly 100%.