Using Serial Analysis it is now possible to obtain quantita tive measurements of the expression of thousands of genes present in a biological sample. Serial analysis yield a global view of gene expression that can be used in a number of interesting ways. In this paper we are investigating two different approaches for analyz ing the analysis of data obtained from SAGE experiments. The first one is a supervised learning process: a classification of cancer tissue using decision trees and Support Vector Machines (SVM). After that, we will analyze the results achieved by a unsupervised learning method: hierar chical clustering. Finally, we tried to characterize the groups found by clustering, using the classification techniques cited before.
Supervised and unsupervised learning techniques for profiling SAGE results.
GAMBERONI, Giacomo;STORARI, Sergio
2004
Abstract
Using Serial Analysis it is now possible to obtain quantita tive measurements of the expression of thousands of genes present in a biological sample. Serial analysis yield a global view of gene expression that can be used in a number of interesting ways. In this paper we are investigating two different approaches for analyz ing the analysis of data obtained from SAGE experiments. The first one is a supervised learning process: a classification of cancer tissue using decision trees and Support Vector Machines (SVM). After that, we will analyze the results achieved by a unsupervised learning method: hierar chical clustering. Finally, we tried to characterize the groups found by clustering, using the classification techniques cited before.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.