Regression models for multivariate count data
Data with multivariate count responses frequently occur in modern applications. The
commonly used multinomial-logit model is limiting due to its restrictive mean-variance …
commonly used multinomial-logit model is limiting due to its restrictive mean-variance …
Comparative genomic evidence for the involvement of schizophrenia risk genes in antipsychotic effects
Genome-wide association studies (GWAS) for schizophrenia have identified over 100 loci
encoding> 500 genes. It is unclear whether any of these genes, other than dopamine …
encoding> 500 genes. It is unclear whether any of these genes, other than dopamine …
Editor's highlight: Comparative dose-response analysis of liver and kidney transcriptomic effects of trichloroethylene and tetrachloroethylene in B6C3F1 mouse
YH Zhou, JA Cichocki, VY Soldatow… - Toxicological …, 2017 - academic.oup.com
Trichloroethylene (TCE) and tetrachloroethylene (PCE) are ubiquitous environmental
contaminants and occupational health hazards. Recent health assessments of these agents …
contaminants and occupational health hazards. Recent health assessments of these agents …
[HTML][HTML] MGLM: an R package for multivariate categorical data analysis
J Kim, Y Zhang, J Day, H Zhou - The R journal, 2018 - ncbi.nlm.nih.gov
Data with multiple responses is ubiquitous in modern applications. However, few tools are
available for regression analysis of multivariate counts. The most popular multinomial-logit …
available for regression analysis of multivariate counts. The most popular multinomial-logit …
[HTML][HTML] Comprehensive benchmark of differential transcript usage analysis for static and dynamic conditions
RNA sequencing offers unique insights into transcriptome diversity, and a plethora of tools
have been developed to analyze alternative splicing. One important task is to detect …
have been developed to analyze alternative splicing. One important task is to detect …
Towards uncovering Transcriptomic Dynamics through Computational Analysis
CT Lio - 2024 - mediatum.ub.tum.de
RNA in cells can be studied computationally using systems biology and dynamic data. Three
key research areas are discussed in this dissertation: 1) the investigation of potential …
key research areas are discussed in this dissertation: 1) the investigation of potential …
Estimating cell type composition using isoform expression one gene at a time
Human tissue samples are often mixtures of heterogeneous cell types, which can confound
the analyses of gene expression data derived from such tissues. The cell type composition …
the analyses of gene expression data derived from such tissues. The cell type composition …
Compressed spectral screening for large-scale differential correlation analysis with application in selecting Glioblastoma gene modules
Compressed spectral screening for large-scale differential correlation analysis with application
in selecting Glioblastoma gene Page 1 The Annals of Applied Statistics 2023, Vol. 17, No. 4 …
in selecting Glioblastoma gene Page 1 The Annals of Applied Statistics 2023, Vol. 17, No. 4 …
Scalable Statistical Methods for Cell Type Deconvolution and Mixed Models Applied to High Dimensional Genomic Data
HM Heiling - 2023 - search.proquest.com
Utilizing genomic data in the clinical setting provides new opportunities for biomarker
discovery, disease characterization, and personalizing treatment, but also poses new …
discovery, disease characterization, and personalizing treatment, but also poses new …
A statistical model to assess (allele-specific) associations between gene expression and epigenetic features using sequencing data
Sequencing techniques have been widely used to assess gene expression (ie, RNA-seq) or
the presence of epigenetic features (eg, DNase-seq to identify open chromatin regions). In …
the presence of epigenetic features (eg, DNase-seq to identify open chromatin regions). In …