Supervised gene identification in colorectal cancer

Abstract

Cancer is a large family of genetic diseases that involve abnormal cell growth. Genetic mutations can vary from one patient to another. Therefore, personalized precision is required to increase the reliability of prognostic predictions and the benefit of therapeutic decisions. The most important issues concerning gene analysis are strong noise, high dimensionality and minor differences between observations. Therefore, parallel coordinates have been also used in order to better analyze the data manifold and select the more meaningful genes. Later, it has been chosen to implement a supervised feature selection algorithm in order to work on a subset of features only avoiding the high dimensional problem. Other traditional methods of dimensionality reduction and projection are here used on subset features in order to better analyze the data manifold and select the more meaningful gene. Previous studies show that mutations of genes NRAS, KRAS and BRAF lead to a dramatic decrease in therapeutic effectiveness. The following analysis tries to explore in an unconventional way gene expressions over tissues which are wild type regarding to these genes.