Predicting liver cancer on epigenomics data using machine learning

Abstract

Epigenomics is the branch of biology concerned with the phenotype modifications that do not induce any change in the cell DNA sequence. The word Epigenomics is made of two-words, epi and genomics; epi comes from the Greek prefix. Epi- in epigenomics implies features that are "on top of" or "in addition to" the traditional genetic basis for inheritance. So, essentially to modify the properties, it applies something to the top of DNA, which ultimately prevents such DNA actions from being executed. These alterations arise in the cancer cells, which is the only cause of cancer. In this research, the objective is to propose a solution for the identification of the best genes of Liver Cancer from gene expression. Four different types of data are used in this research to predict cancerous cells in LIHC (liver hepatocellular carcinoma) patients. The data includes methylation, histone, the human genome, and RNA-Sequences. The data is accessed through open-source technologies in R programming language. The data is processed to create features, and with the help of statistical analysis and advanced machine learning techniques, the prediction of liver cancer is obtained from the fine-tuned model.