The combined analysis of the microarray and drug-activity datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activity patterns in tumor cells. However, the huge amount of biological data needs appropriate data mining models in order to extract interesting patterns and useful information. In this paper, the NCI60 dataset has been analyzed for the molecular pharmacology of cancer. In particular, we proposed a novel relational clustering algorithm joint with bayesian network inference engine for linking gene expression profiles to drug activity patterns. Our analysis could be an initial step for predicting potential usefull drugs according to the gene expression level of tumor tissues

Archetti, F., Fersini, E., Giordani, I., Messina, V. (2009). Relational clustering and Bayesian networks for linking gene expression profiles and drug activity patterns. In BIBMW: 2009 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOP (pp.20-25). IEEE [10.1109/BIBMW.2009.5332144].

Relational clustering and Bayesian networks for linking gene expression profiles and drug activity patterns

ARCHETTI, FRANCESCO ANTONIO;FERSINI, ELISABETTA;GIORDANI, ILARIA;MESSINA, VINCENZINA
2009

Abstract

The combined analysis of the microarray and drug-activity datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activity patterns in tumor cells. However, the huge amount of biological data needs appropriate data mining models in order to extract interesting patterns and useful information. In this paper, the NCI60 dataset has been analyzed for the molecular pharmacology of cancer. In particular, we proposed a novel relational clustering algorithm joint with bayesian network inference engine for linking gene expression profiles to drug activity patterns. Our analysis could be an initial step for predicting potential usefull drugs according to the gene expression level of tumor tissues
No
paper
clustering; bayesian networks; gene expression; drug activity
English
IEEE International Conference on Bioinformatics and Biomedicine (BIBMW 2009) NOV 01-04
978-1-4244-5121-0
Archetti, F., Fersini, E., Giordani, I., Messina, V. (2009). Relational clustering and Bayesian networks for linking gene expression profiles and drug activity patterns. In BIBMW: 2009 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOP (pp.20-25). IEEE [10.1109/BIBMW.2009.5332144].
Archetti, F; Fersini, E; Giordani, I; Messina, V
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/8903
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