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.
2004
sage; data mining; clustering
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/1189388
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