Tanzy M. T. Love

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There has been an explosive growth of data-mining models involving latent structure for clustering and classification. While having related objectives these models use different parameter-izations and often very different specifications and constraints. Model choice is thus a major methodological issue and a crucial practical one for applications. In this(More)
Model choice is a major methodological issue in the explosive growth of data-mining models involving latent structure for clustering and classification, especially because models often have different parameterizations and very different specifications and constraints. Here, we work from a general formulation of hierarchical Bayesian mixed-membership models(More)
Gene expression patterns were profiled during somatic embryogenesis in a regeneration-proficient maize hybrid line, Hi II, in an effort to identify genes that might be used as developmental markers or targets to optimize regeneration steps for recovering maize plants from tissue culture. Gene expression profiles were generated from embryogenic calli induced(More)
BACKGROUND Dental amalgam is approximately 50% metallic mercury and releases mercury vapor into the oral cavity, where it is inhaled and absorbed. Maternal amalgams expose the developing fetus to mercury vapor. Mercury vapor can be toxic, but uncertainty remains whether prenatal amalgam exposure is associated with neurodevelopmental consequences in(More)
PNAS article classification is rooted in long-standing disciplinary divisions that do not necessarily reflect the structure of modern scientific research. We reevaluate that structure using latent pattern models from statistical machine learning, also known as mixed-membership models, that identify semantic structure in co-occurrence of words in the(More)
Limited human data are available to assess the association between prenatal mercury vapor (Hg⁰)) exposure from maternal dental amalgam restorations and neurodevelopment of children. We evaluated the association between maternal dental amalgam status during gestation and children's neurodevelopmental outcomes at 5 years in the Seychelles Child Development(More)
Hierarchical Bayesian methods expanded markedly with the introduction of MCMC computation in the 1980s, and this was followed by the explosive growth of machine learning tools involving latent structure for clustering and classification. Nonetheless, model choice remains a major methodological issue, largely because competing models used in machine learning(More)
Most individuals with constitutional deletions of chromosome 18q have developmental delays, dysmyelination of the brain and growth failure due to growth hormone deficiency. We monitored the effects of growth hormone treatment by evaluating 23 individuals for changes in growth, performance intelligence quotient (pIQ) and quantitative brain MRI changes. Over(More)
BACKGROUND Improved genetic resolution and availability of sequenced genomes have made positional cloning of moderate-effect QTL realistic in several systems, emphasizing the need for precise and accurate derivation of positional confidence intervals (CIs) for QTL. Support interval (SI) methods based on the shape of the QTL likelihood curve have proven(More)
We consider regression models for multiple correlated outcomes, where the outcomes are nested in domains. We show that random effect models for this nested situation fit into a standard factor model framework, which leads us to view the modeling options as a spectrum between parsimonious random effect multiple outcomes models and more general continuous(More)