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.
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Gamberoni, G., Lamma, E., Riguzzi, F., Storari, S., Volinia, S. (2005). Bayesian Networks Learning for Gene Expression Datasets. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds) Advances in Intelligent Data Analysis VI. IDA 2005. Lecture Notes in Computer Science, vol 3646. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552253_11
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DOI: https://doi.org/10.1007/11552253_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28795-7
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