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Immune System Modeling with Infer.NET

Vincent Y. F. Tan, John Winn, Angela Simpson, and Adnan Custovic


Graphical models allow scientific prior knowledge to be incorporated into the statistical analysis of data, whilst also providing a vivid way to represent and communicate this knowledge. In this paper we develop a graphical model of the immune system as a means of analyzing immunological data from the Manchester Asthma and Allergy Study (MAAS). The analysis is achieved using the Infer.NET tool which allows Bayesian inference to be applied automatically to a specified graphical model. Our immune system model consists firstly of a Hidden Markov Model representing how allergen-specific skin prick tests (SPTs) and serum-specific IgE tests (SITs) change over time. By introducing a latent multinomial variable, we also cluster the children in an unsupervised manner into different sensitization classes. For 2 sensitization classes, the children who are vulnerable to allergies and have a high probability of having asthma (22%) are identified. For 5 sensitization classes, children in the first cluster, those who are vulnerable to allergies, have an even higher probability of having asthma (42%). The second part of the model involves using the inferred sensitization class as a label and 8 exposure variables in a Bayes Point Machine. Using multiple permutation tests, we conclude that the level of endotoxins and gender have a significant effect on a child’s vulnerability to allergies.


Publication typeInproceedings
Published inIEEE International Conference on e-Science (e-Science 2008),
AddressIndianapolis, Indiana
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