Semi-supervised learning of hierarchical latent trait models for data visualisation

An interactive hierarchical Generative Topographic Mapping (HGTM) ¸iteH_GTM has been developed to visualise complex data sets. In this paper, we build a more general visualisation system by extending the HGTM visualisation system in 3 directions: bf (1) We generalize HGTM to noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM) developed in ¸iteKaban_pami. bf (2) We give the user a choice of initializing the child plots of the current plot in either em interactive, or em automatic mode. In the interactive mode the user interactively selects ``regions of interest'' as in ¸iteH_GTM, whereas in the automatic mode an unsupervised minimum message length (MML)-driven construction of a mixture of LTMs is employed. bf (3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualisation plots, since they can highlight the boundaries between data clusters. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. We illustrate our approach on a toy example and apply our system to three more complex real data sets.

Can certain genotypes predispose to poor asthma control in children? A pharmacogenetic study of 9 candidate genes in children with difficult asthma

Objective - We tested the hypothesis that patients with difficult asthma have an increased frequency of certain genotypes that predispose them to asthma exacerbations and poor asthma control. Methods - A total of 180 Caucasian children with confirmed asthma diagnosis were selected from two phenotypic groups; difficult (n = 112) versus mild/moderate asthma (n = 68) groups. All patients were screened for 19 polymorphisms in 9 candidate genes to evaluate their association with difficult asthma. Key Results - The results indicated that LTA4H A-9188>G, TNFα G-308>A and IL-4Rα A1727>G polymorphisms were significantly associated with the development of difficult asthma in paediatric patients (p<0.001, p = 0.019 and p = 0.037, respectively). Haplotype analysis also revealed two haplotypes (ATA haplotype of IL-4Rα A1199>C, IL-4Rα T1570>C and IL-4Rα A1727>G and CA haplotype of TNFα C-863>A and TNFα G-308>A polymorphisms) which were significantly associated with difficult asthma in children (p = 0.04 and p = 0.018, respectively). Conclusions and Clinical Relevance - The study revealed multiple SNPs and haplotypes in LTA4H, TNFα and IL4-Rα genes which constitute risk factors for the development of difficult asthma in children. Of particular interest is the LTA4H A-9188>G polymorphism which has been reported, for the first time, to have strong association with severe asthma in children. Our results suggest that screening for patients with this genetic marker could help characterise the heterogeneity of responses to leukotriene-modifying medications and, hence, facilitate targeting these therapies to the subset of patients who are most likely to gain benefit.

Semisupervised learning of hierarchical latent trait models for data visualization

Recently, we have developed the hierarchical Generative Topographic Mapping (HGTM), an interactive method for visualization of large high-dimensional real-valued data sets. In this paper, we propose a more general visualization system by extending HGTM in three ways, which allows the user to visualize a wider range of data sets and better support the model development process. 1) We integrate HGTM with noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM). This enables us to visualize data of inherently discrete nature, e.g., collections of documents, in a hierarchical manner. 2) We give the user a choice of initializing the child plots of the current plot in either interactive, or automatic mode. In the interactive mode, the user selects "regions of interest," whereas in the automatic mode, an unsupervised minimum message length (MML)-inspired construction of a mixture of LTMs is employed. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. 3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualization plots, since they can highlight the boundaries between data clusters. We illustrate our approach on a toy example and evaluate it on three more complex real data sets. © 2005 IEEE.