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Reverse engineering gene and protein regulatory networks using graphical models: A comparative evaluation study

Reverse engineering gene and protein regulatory networks using graphical models: A comparative evaluation study

This video was recorded at Workshop on Probabilistic Modelling of Networks and Pathways, Sheffield 2007. One of the major goals in systems biology is to infer the architecture of biochemical pathways and regulatory networks from postgenomic data, such as microarray gene expression and cytometric protein expression data. Various reverse engineering Machine Learning methods have been proposed in the literature, and it is important to understand their relative merits and shortcomings. In the talk the learning performances of three different graphical models machine learning methods, namely Relevance networks, Gaussian Graphical Models, and Bayesian networks, are cross-compared on real cytometric protein data and simulated data from the RAF signalling pathway. Relevance networks are based on... Show More

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