Bayesian Networks Learning for Gene Expression Datasets

• Published in: Advances in Intelligent Data Analysis VI pp. 109 - 120
• Main Authors: Gamberoni, Giacomo, Lamma, Evelina, Riguzzi, Fabrizio, Storari, Sergio, Volinia, Stefano
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Acute Myeloid Leukemia; Applied sciences; Artificial intelligence; Association Rule; Bayesian Network; Causal Network; Computer science; control theory; systems; Data processing. List processing. Character string processing; Exact sciences and technology; Gene Expression Dataset; Memory organisation. Data processing; Software; Human; Data analysis; Leukemia; DNA chip; Gene expression; Modeling; Genetic algorithm; DNA; Bayes network; Genetics; Genetic engineering; Microprocessor; Learning algorithm; Bioinformatics; Artificial intelligence
• ISBN: 9783540287957; 3540287957
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11552253_11

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.