COMPREHENDING THE EFFECT OF SMOKING IN LUNG CANCER BY USING RULE NETWORKS

Abstract: With advances in computer science, computational analytics and more specifically machine learning algorithms are increasingly being used in many fields of science. These algorithms are highly valuable for the analysis of large and complex biological data that has to be performed to convey the information the raw data contains so that it can be further interpreted. Cancer is one of the most significant health problems of our day due to its huge burden on mortality, quality of life and health expenditures. The etiology and mechanism of many types of cancer are yet to be elucidated, therefore research on the subject is of paramount importance. Lung cancer especially is of great significance because of high incidence and rate of mortality, being the leading cause of cancer-related deaths. Although smoking is considered as the most prominent risk factor for lung cancer, its mechanism of action has not been figured out comprehensively yet. The aim of this project is using machine learning algorithms to generate rule-based models and create rule networks which can help better understand the effects of smoking on gene expression and metabolite levels that are potentially associated with lung cancer. For this task, we tried two feature selection algorithms (Boruta and Monte Carlo) and the ROSETTA software to generate rule-based classifier models. The resulting rule networks were schematically visualized using VisuNet.