Bayesian statistics and modelling
Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …
available knowledge about parameters in a statistical model is updated with the information …
A selective review of multi-level omics data integration using variable selection
High-throughput technologies have been used to generate a large amount of omics data. In
the past, single-level analysis has been extensively conducted where the omics …
the past, single-level analysis has been extensively conducted where the omics …
A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data
Z Yang, G Michailidis - Bioinformatics, 2016 - academic.oup.com
Motivation: Recent advances in high-throughput omics technologies have enabled
biomedical researchers to collect large-scale genomic data. As a consequence, there has …
biomedical researchers to collect large-scale genomic data. As a consequence, there has …
Bayesian variable selection and estimation for group lasso
X Xu, M Ghosh - 2015 - projecteuclid.org
The paper revisits the Bayesian group lasso and uses spike and slab priors for group
variable selection. In the process, the connection of our model with penalized regression is …
variable selection. In the process, the connection of our model with penalized regression is …
DNA methylation as a mediator of genetic and environmental influences on Parkinson's disease susceptibility: Impacts of alpha-Synuclein, physical activity, and …
Parkinson's disease (PD) is a neurodegenerative disorder with a complex etiology and
increasing prevalence worldwide. As PD is influenced by a combination of genetic and …
increasing prevalence worldwide. As PD is influenced by a combination of genetic and …
Statistical methods in integrative genomics
Statistical methods in integrative genomics aim to answer important biology questions by
jointly analyzing multiple types of genomic data (vertical integration) or aggregating the …
jointly analyzing multiple types of genomic data (vertical integration) or aggregating the …
Hierarchical shrinkage priors for regression models
Hierarchical Shrinkage Priors for Regression Models Page 1 Bayesian Analysis (2017) 12,
Number 1, pp. 135–159 Hierarchical Shrinkage Priors for Regression Models Jim Griffin ∗ and …
Number 1, pp. 135–159 Hierarchical Shrinkage Priors for Regression Models Jim Griffin ∗ and …
A Bayesian approach for estimating dynamic functional network connectivity in fMRI data
Dynamic functional connectivity, that is, the study of how interactions among brain regions
change dynamically over the course of an fMRI experiment, has recently received wide …
change dynamically over the course of an fMRI experiment, has recently received wide …
Spatial Bayesian variable selection and grou** for high-dimensional scalar-on-image regression
Spatial Bayesian variable selection and grou** for high-dimensional scalar-on-image
regression Page 1 The Annals of Applied Statistics 2015, Vol. 9, No. 2, 687–713 DOI …
regression Page 1 The Annals of Applied Statistics 2015, Vol. 9, No. 2, 687–713 DOI …
Bayesian graphical models for modern biological applications
Graphical models are powerful tools that are regularly used to investigate complex
dependence structures in high-throughput biomedical datasets. They allow for holistic …
dependence structures in high-throughput biomedical datasets. They allow for holistic …