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Zero-Inflated Beta Models for Microbiome Data
This chapter introduces two specifically designed zero-inflated beta regression models for analyzing zero-inflated count microbiome data. First, it...
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Estimating the total variance explained by whole-brain imaging for zero-inflated outcomes
There is a dearth of statistical models that adequately capture the total signal attributed to whole-brain imaging features. The total signal is...
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Randomized quantile residuals for diagnosing zero-inflated generalized linear mixed models with applications to microbiome count data
BackgroundFor differential abundance analysis, zero-inflated generalized linear models, typically zero-inflated NB models, have been increasingly...
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Emergent analogical reasoning in large language models
The recent advent of large language models has reinvigorated debate over whether human cognitive capacities might emerge in such generic models given...
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Implications of zero-deforestation palm oil for tropical grassy and dry forest biodiversity
Many companies have made zero-deforestation commitments (ZDCs) to reduce carbon emissions and biodiversity losses linked to tropical commodities....
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Modeling zero inflation is not necessary for spatial transcriptomics
BackgroundSpatial transcriptomics are a set of new technologies that profile gene expression on tissues with spatial localization information. With...
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Thickness of zero-strength layer in timber beam exposed to fuel-controlled parametric fires
Fire resistance is one of the essential requirements to be fulfilled in the design of timber structures. For this purpose, a reduced cross-section...
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A recurring error in evaluating the effects of different pesticides, pollutants and fertilizers with a zero level
BackgroundThe Quenouille-Addelman solution has been proposed to properly analyze linear models with a crossed or factorial arrangement of treatments...
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A zero-inflated model for spatiotemporal count data with extra zeros: application to 1950–2015 tornado data in Kansas
In many tornado climate studies, the number of tornado touchdowns is often the primary outcome of interest. These outcome measures are usually...
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Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering
Mutations in amino acid sequences can provoke changes in protein function. Accurate and unsupervised prediction of mutation effects is critical in...
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CREaTor: zero-shot cis-regulatory pattern modeling with attention mechanisms
Linking cis -regulatory sequences to target genes has been a long-standing challenge. In this study, we introduce CREaTor, an attention-based deep...
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NBZIMM: negative binomial and zero-inflated mixed models, with application to microbiome/metagenomics data analysis
BackgroundMicrobiome/metagenomic data have specific characteristics, including varying total sequence reads, over-dispersion, and zero-inflation,...
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Leveraging pre-trained language models for mining microbiome-disease relationships
BackgroundThe growing recognition of the microbiome’s impact on human health and well-being has prompted extensive research into discovering the...
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Generalized Linear Mixed Models for Longitudinal Microbiome Data
Chapter 16 investigated some general topics of generalized linear mixed-effects models (GLMMs). This chapter...
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Application and Comparison of Different Regression Models in Iodine Balance Experiment on Women of Childbearing Age and Pregnant Women
The iodine balance experiment is a traditional approach to evaluate the physiological requirement for iodine, while the simple linear regression...
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On the Way to Circular Economy: Türkiye’s Waste Management and Zero Waste Project
Türkiye is a bridge and the crossroad between the Middle East and Asia and Europe. The economical and social growth gets Türkiye as a good sample and...
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A Soft-Thresholding Operator for Sparse Time-Varying Effects in Survival Models
We consider a class of Cox models with time-dependent effects that may be zero over certain unknown time regions or, in short, sparse time-varying...
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Occupancy models with autocorrelated detection heterogeneity
Occupancy models are commonly used in statistical ecology to model binary detection/non-detection data. These hierarchical models make a distinction...
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Functional and variables selection in extreme value models for regional flood frequency analysis
The problem of estimating return levels of river discharge, relevant in flood frequency analysis, is tackled by relying on the extreme value theory....
