Towards Precision Medicine for Cancer Patient Stratification by Classifying Cancer by Using Machine Learning

On average, a drug or a treatment is effective in only about half of patients who take it. These patients need to try several until they find one that is effective at the cost of side effects associated with every treatment. The ultimate goal of precision medicine is to provide a treatment best suited for every individual. Sequencing technologies have now made genome means ics data available in abundance to be used towards this goal. In this project, we will specifically focus on cancer. Most cancer patients get a particular treatment based on the cancer type and the stage, though different individuals will react differently to a treatment. It is now well established that genetic mutations cause cancer growth and spreading and importantly, these mutations are different in individual patients. The aim of this project is to use genomic data to allow for better stratification of cancer patients, to predict the treatment most likely to work. Specifically, the project will use a machine learning approach to classify cancer and suggest medicine. The whole work is divided into two parts, one is predicting cancer using several machine learning classification techniques and then suggesting medicine.