Exhibits, Demos & Posters
Immune System Modeling with Infer.NET
Authors
- Vincent Tan, Laboratory for Information and Decision Systems, Massachusetts Institute of Technology
- John Winn, Microsoft Research Cambridge, United Kingdom
- Angela Simpson, School of Medicine, University of Manchester
- Adnan Custovic, School of Medicine, University of Manchester
Abstract
Graphical models allow scientific prior knowledge to be incorporated into the statistical analysis of data, while 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 two sensitization classes, the children who are vulnerable to allergies and have a high probability of having asthma (22%) are identified. For five 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 eight 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.
