The combined analysis of tissue micro array and drug response datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activity patterns in tumor cells. However, the amount and the complexity of biological data needs appropriate data mining models in order to extract interesting patterns and useful information. The ultimate goal of this paper is to define a model which, given the gene expression profile related to a specific tumor tissue, could help in selecting a set of most responsive drugs. This is accomplished through an integrated framework based on a constraint-based clustering algorithm, called Relational K-Means, which groups cell lines using drug response information and taking into account cell-to-cell relationships derived from their gene expression profiles. © 2010 Springer Science+Business Media B.V.

Fersini, E., Messina, V., Archetti, F., Manfredotti, C. (2010). Combining Gene Expression Profiles and Drug Activity Patterns Analysis: A Relational Clustering Approach. JOURNAL OF MATHEMATICAL MODELLING AND ALGORITHMS, 9(3), 275-289 [10.1007/s10852-010-9140-2].

Combining Gene Expression Profiles and Drug Activity Patterns Analysis: A Relational Clustering Approach

FERSINI, ELISABETTA;MESSINA, VINCENZINA;ARCHETTI, FRANCESCO ANTONIO;MANFREDOTTI, CRISTINA ELENA
2010

Abstract

The combined analysis of tissue micro array and drug response datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activity patterns in tumor cells. However, the amount and the complexity of biological data needs appropriate data mining models in order to extract interesting patterns and useful information. The ultimate goal of this paper is to define a model which, given the gene expression profile related to a specific tumor tissue, could help in selecting a set of most responsive drugs. This is accomplished through an integrated framework based on a constraint-based clustering algorithm, called Relational K-Means, which groups cell lines using drug response information and taking into account cell-to-cell relationships derived from their gene expression profiles. © 2010 Springer Science+Business Media B.V.
Articolo in rivista - Articolo scientifico
Relational clustering; Pharmacogenomics; NCI60 dataset analysis;
English
275
289
Fersini, E., Messina, V., Archetti, F., Manfredotti, C. (2010). Combining Gene Expression Profiles and Drug Activity Patterns Analysis: A Relational Clustering Approach. JOURNAL OF MATHEMATICAL MODELLING AND ALGORITHMS, 9(3), 275-289 [10.1007/s10852-010-9140-2].
Fersini, E; Messina, V; Archetti, F; Manfredotti, C
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/22481
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