Exploiting supervised and unsupervised learning techniques for profiling cancer data

Through the use of DNA microarray it is now possible to obtain quantitative measurements of the expression of thousands of genes present in a biological sample. DNA arrays yield a global view of gene expression and they can be used in a number of interesting ways. In this paper we are investigating two different approaches for analyzing the analysis of data obtained from microarray experiments. The first one is a supervised learning process: a classification of cancer tissue using decision trees and Support Vector Machines (SVM). We constructed decision trees using the c4.5 algorithm. SVM are linear models in the space of attributes that can discriminate two classes of examples. After that, we will analyze the results achieved by a unsupervised learning method: clustering. This algorithm regroups set of examples that present similar characteristics, in particular, hierarchical clustering does it iteratively, starting from considering each sample as a single set.