Machine learning for identification and characterization of molecular gene signatures in progression of benign tumors

Kidney cancer is one of the most common cancer with a 2.02% risk in men and 1.02% in women. While several attempts are being made in improving early diagnosis, artificial intelligence has been successful in prediction of cancer progression. This paper utilizes artificial intelligence to classify genome-wide CpG regions for the identification of novel genes in the pathogenesis of benign and malignant cancer. Briefly, random forest is utilized for narrowing down the number of genes and later classify them with the aim of generating a diagnostic and prognostic biomarker signature. Characterizing molecular pathophysiology of identified genes with linking the methylation changes and its causative effects in renal cysts and masses development and progression is important for validation of proposed technique.