Data Mining Approaches for Identifying Genetic Markers in Cancer

Abstract

Cancer is a complex disease driven by genetic mutations that lead to uncontrolled cell growth. Identifying genetic markers associated with cancer can aid in early diagnosis, prognosis, and the development of personalized treatments. Data mining techniques have become a valuable tool in identifying these markers from large-scale genomic datasets. This article explores various data mining approaches, including supervised and unsupervised learning, feature selection, and clustering methods, used to identify genetic markers in cancer. We also discuss the challenges in cancer genetic marker discovery and the potential applications of these markers in cancer treatment and personalized medicine.