Bayesian networks learning for gene expression datasets

Authors:
Giacomo Gamberoni;Evelina Lamma;Fabrizio Riguzzi;Sergio Storari;Stefano Volinia
Affiliations:
ENDIF-Dipartimento di Ingegneria, Università di Ferrara, Ferrara, Italy;ENDIF-Dipartimento di Ingegneria, Università di Ferrara, Ferrara, Italy;ENDIF-Dipartimento di Ingegneria, Università di Ferrara, Ferrara, Italy;ENDIF-Dipartimento di Ingegneria, Università di Ferrara, Ferrara, Italy;Dipartimento di Biologia, Università di Ferrara, Ferrara, Italy
Venue:
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Year:
2005

Citing 6
Cited 1

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Machine Learning
Data Mining Techniques: For Marketing, Sales, and Customer Support

Data Mining Techniques: For Marketing, Sales, and Customer Support
Learning Bayesian networks from data: an information-theory based approach

Artificial Intelligence

Exploiting association and correlation rules parameters for learning Bayesian networks

Intelligent Data Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

DNA arrays yield a global view of gene expression and can be used to build genetic networks models, in order to study relations between genes. Literature proposes Bayesian network as an appropriate tool for develop similar models. In this paper, we exploit the contribute of two Bayesian network learning algorithms to generate genetic networks from microarray datasets of experiments performed on Acute Myeloid Leukemia (AML). In the results, we present an analysis protocol used to synthesize knowledge about the most interesting gene interactions and compare the networks learned by the two algorithms. We also evaluated relations found in these models with the ones found by biological studies performed on AML.