Authors: Nikolaos-Modestos Kougioulis
In this report, we present Bayesian networks, a seminal class of graphical models in the Artificial Intelligence field (Pearl, 1982;1988), and as a result Causal Networks (Pearl, 2000), as a natural mathematical theory for modelling dependence relationships between random variables and inference. Algorithms for the construction of these models are presented in an analytic manner. We introduce the fields of Statistical Learning and Bioinformatics, and make emphasis on the microarray technology. Bayesian networks modelling can be applied to construct Gene Regulatory Networks from data. From this, we are able to gain insight on the regulation mechanisms between the genes and-or proteins. As an example, the protein-signaling network constructed by Scutari & Denis (2014) and Nagarajan et al. (2013) using biological data from Sachs et al. (2005) is presented. Using the microarray data from Gordon et al (2002) we propose the Naive Bayes Classifier as a suitable predictor for the diagnosis and distinction of Adenocarcynoma and Mesothelioma based on gene expression Data from tumor samples. The R programming language (R Development Team, 2012) is used for both applications.
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