Molecular classification of malignancies can potentially stratify patients into distinct subclasses not detectable using traditional classification of tumors, opening new perspectives on the diagnosis and personalized therapy of polygenic diseases. In this paper we present a brief overview of our work on gene expression based prediction of malignancies, starting from the dichotomic classification problem of normal versus tumoural tissues, to multiclasss cancer diagnosis and to functional class discovery and gene selection problems. The last part of this work present preliminary results about the applicatin of ensembles of SVMs based on bias-variance decomposition of the error to the analysis of gene expression data of malignant tissues.

Gene expression-based prediction of malignancies / G. Valentini. - In: AIIA NOTIZIE. - 15:4(2002), pp. 34-38. ((Intervento presentato al 7. convegno AI*IA Workshop sulla Bioinformatica. Congresso dell'Associazione Italiana per l'Intelligenza Artificiale tenutosi a Siena nel 2002.

Gene expression-based prediction of malignancies

G. Valentini
Primo
2002

Abstract

Molecular classification of malignancies can potentially stratify patients into distinct subclasses not detectable using traditional classification of tumors, opening new perspectives on the diagnosis and personalized therapy of polygenic diseases. In this paper we present a brief overview of our work on gene expression based prediction of malignancies, starting from the dichotomic classification problem of normal versus tumoural tissues, to multiclasss cancer diagnosis and to functional class discovery and gene selection problems. The last part of this work present preliminary results about the applicatin of ensembles of SVMs based on bias-variance decomposition of the error to the analysis of gene expression data of malignant tissues.
Settore INF/01 - Informatica
2002
Associazione Italiana per l'Intelligenza Artificiale
http://www.dsi.unifi.it/AIIA/ABSTRACT/paper404_02.htm
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/175570
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