Leanna House

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Host-microbe symbioses rely on the successful transmission or acquisition of symbionts in each new generation. Amphibians host a diverse cutaneous microbiota, and many of these symbionts appear to be mutualistic and may limit infection by the chytrid fungus, Batrachochytrium dendrobatidis, which has caused global amphibian population declines and(More)
In visual analytics, sensemaking is facilitated through interactive visual exploration of data. Throughout this dynamic process, users combine their domain knowledge with the dataset to create insight. Therefore, visual analytic tools exist that aid sensemaking by providing various interaction techniques that focus on allowing users to change the visual(More)
Vertebrates, including amphibians, host diverse symbiotic microbes that contribute to host disease resistance. Globally, and especially in montane tropical systems, many amphibian species are threatened by a chytrid fungus, Batrachochytrium dendrobatidis (Bd), that causes a lethal skin disease. Bd therefore may be a strong selective agent on the diversity(More)
Semantic interaction offers an intuitive communication mechanism between human users and complex statistical models. By shielding the users from manipulating model parameters, they focus instead on directly manipulating the spatialization, thus remaining in their cognitive zone. However, this technique is not inherently scalable past hundreds of text(More)
The vertebrate microbiome contributes to disease resistance, but few experiments have examined the link between microbiome community structure and disease resistance functions. Chytridiomycosis, a major cause of amphibian population declines, is a skin disease caused by the fungus, Batrachochytrium dendrobatidis (Bd). In a factorial experiment, bullfrog(More)
We present model-based inference for proteomic peak identification and quantification from mass spectroscopy data, focusing on nonparametric Bayesian models. Using experimental data generated from MALDI-TOF mass spectroscopy (Matrix Assisted Laser Desorption Ionization Time of Flight) we model observed intensities in spectra with a hierarchical(More)
We present a novel nonparametric Bayesian approach based on Lévy Adaptive Regression Kernels (LARK) to model spectral data arising from MALDI-TOF (Matrix Assisted Laser Desorption Ionization Time-of-Flight) mass spectrometry. This model based approach provides identification and quantification of proteins through model parameters that are directly(More)
When high-dimensional data is visualized in a 2D plane by using parametric projection algorithms, users may wish to manipulate the layout of the data points to better reflect their domain knowledge or to explore alternative structures. However, few users are well-versed in the algorithms behind the visualizations, making parameter tweaking more of a(More)
Typical data visualizations result from linear pipelines that start by characterizing data using a model or algorithm to reduce the dimension and summarize structure, and end by displaying the data in a reduced dimensional form. Sensemaking may take place at the end of the pipeline when users have an opportunity to observe, digest, and internalize any(More)
Large, high dimensional datasets generally contain information in small, concentrated regions of the data space. To extract this information, it is necessary to draw on several fields and use a variety of tools. We develop a new analytics framework that merges two areas of research, Bayesian Statistics and Visual Analytics. Mathematical and statistical(More)