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Bayesian discrete lognormal regression model for genomic prediction
Key messageGenomic prediction models for quantitative traits assume continuous and normally distributed phenotypes. In this research, we proposed a...
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A hierarchical Bayesian model to monitor pelagic larvae in response to environmental changes
European anchovies and round sardinella play a crucial role, both ecological and commercial, in the Mediterranean Sea. In this paper, we investigate...
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Correlations reveal the hierarchical organization of biological networks with latent variables
Deciphering the functional organization of large biological networks is a major challenge for current mathematical methods. A common approach is to...
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Capture-Recapture: Bayesian Methods
This chapter outlines the development of Bayesian methods for estimating abundance in closed population capture-recapture models. We describe model...
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Bayesian variable selection for high-dimensional data with an ordinal response: identifying genes associated with prognostic risk group in acute myeloid leukemia
BackgroundAcute myeloid leukemia (AML) is a heterogeneous cancer of the blood, though specific recurring cytogenetic abnormalities in AML are...
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Application of a Bayesian structural time series model for evaluating 11-year variation in pH in the headwaters of the Tama River, Japan
Understanding the temporal variations that occur in river water pH is vital for the conservation of aquatic organisms in riverine ecosystems....
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Pitfalls and opportunities for applying latent variables in single-cell eQTL analyses
Using latent variables in gene expression data can help correct unobserved confounders and increase statistical power for expression quantitative...
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Bayesian compositional regression with microbiome features via variational inference
The microbiome plays a key role in the health of the human body. Interest often lies in finding features of the microbiome, alongside other...
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Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes
BackgroundHeterogeneity in the definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and...
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Bayesian Optimization in Drug Discovery
Drug discovery deals with the search for initial hits and their optimization toward a targeted clinical profile. Throughout the discovery pipeline,...
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GBDR: a Bayesian model for precise prediction of pathogenic microorganisms using 16S rRNA gene sequences
BackgroundRecent evidences have suggested that human microorganisms participate in important biological activities in the human body. The dysfunction...
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Model-based entropy estimation for data with covariates and dependence structures
Entropy is widely used in ecological and environmental studies, where data often present complex interactions. Difficulties arise in linking entropy...
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Genotype-by-environment interaction of wheat using Bayesian factor analytic models and environmental covariates
Wheat ( Triticum aestivum L . ) plays a significant role in the global agricultural economy. Breeding programs need to identify environments where...
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Bayesian stroke modeling details sex biases in the white matter substrates of aphasia
Ischemic cerebrovascular events often lead to aphasia. Previous work provided hints that such strokes may affect women and men in distinct ways....
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On Bayesian modeling of censored data in JAGS
BackgroundJust Another Gibbs Sampling (JAGS) is a convenient tool to draw posterior samples using Markov Chain Monte Carlo for Bayesian modeling....
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Consensus clustering for Bayesian mixture models
BackgroundCluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules....
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Model Building
This introductory chapter is about fundamental ideas involved in model selection such as between a model-based or design-based approach, or a...
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Application of Bayesian genomic prediction methods to genome-wide association analyses
BackgroundBayesian genomic prediction methods were developed to simultaneously fit all genotyped markers to a set of available phenotypes for...
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Comparing maximum likelihood and Bayesian methods for fitting hidden Markov models to multi-state capture-recapture data of invasive carp in the Illinois River
BackgroundHidden Markov Models (HMMs) are often used to model multi-state capture-recapture data in ecology. However, a variety of HMM modeling...
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Bayesian latent class evaluation of three tests for the screening of subclinical caprine mastitis in Bangladesh
Routine monitoring for subclinical infection is one of the key mastitis control approaches. However, the accuracy of the most commonly used screening...