Model-based multifacet clustering with high-dimensional omics applications [0.03%]
基于模型的多角度聚类在高维组学中的应用
Wei Zong,Danyang Li,Marianne L Seney et al.
Wei Zong et al.
High-dimensional omics data often contain intricate and multifaceted information, resulting in the coexistence of multiple plausible sample partitions based on different subsets of selected features. Conventional clustering methods typicall...
Bayesian estimation of covariate assisted principal regression for brain functional connectivity [0.03%]
借助辅助变量的脑功能连接的贝叶斯主成分回归分析方法
Hyung G Park
Hyung G Park
This paper presents a Bayesian reformulation of covariate-assisted principal regression for covariance matrix outcomes to identify low-dimensional components in the covariance associated with covariates. By introducing a geometric approach ...
Thai-Son Tang,Zhihui Liu,Ali Hosni et al.
Thai-Son Tang et al.
The goal of radiation therapy for cancer is to deliver prescribed radiation dose to the tumor while minimizing dose to the surrounding healthy tissues. To evaluate treatment plans, the dose distribution to healthy organs is commonly summari...
A modeling framework for detecting and leveraging node-level information in Bayesian network inference [0.03%]
一种在贝叶斯网络推断中检测和利用节点层次信息的模型框架
Xiaoyue Xi,Hélène Ruffieux
Xiaoyue Xi
Bayesian graphical models are powerful tools to infer complex relationships in high dimension, yet are often fraught with computational and statistical challenges. If exploited in a principled way, the increasing information collected along...
DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes [0.03%]
差异调控分析：一种识别差异调控基因的贝叶斯分层方法
Simone Tiberi,Joël Meili,Peiying Cai et al.
Simone Tiberi et al.
Although transcriptomics data is typically used to analyze mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene...
A joint normal-ordinal (probit) model for ordinal and continuous longitudinal data [0.03%]
联合正态-序数（ probit ）模型在有序和连续纵向数据中的应用
Margaux Delporte,Geert Molenberghs,Steffen Fieuws et al.
Margaux Delporte et al.
In biomedical studies, continuous and ordinal longitudinal variables are frequently encountered. In many of these studies it is of interest to estimate the effect of one of these longitudinal variables on the other. Time-dependent covariate...
Fast matrix completion in epigenetic methylation studies with informative covariates [0.03%]
具有指导性协变量的表观遗传学甲基化研究中的快速矩阵填充方法
Mélina Ribaud,Aurélie Labbe,Khaled Fouda et al.
Mélina Ribaud et al.
DNA methylation is an important epigenetic mark that modulates gene expression through the inhibition of transcriptional proteins binding to DNA. As in many other omics experiments, the issue of missing values is an important one, and appro...
Simultaneous clustering and estimation of networks in multiple graphical models [0.03%]
多重图形模型中的网络同时聚类和估计问题
Gen Li,Miaoyan Wang
Gen Li
Gaussian graphical models are widely used to study the dependence structure among variables. When samples are obtained from multiple conditions or populations, joint analysis of multiple graphical models are desired due to their capacity to...
Bayesian joint modeling of multivariate longitudinal and survival outcomes using Gaussian copulas [0.03%]
基于高斯Copula的多元纵向和生存数据联合贝叶斯分析方法研究
Seoyoon Cho,Matthew A Psioda,Joseph G Ibrahim
Seoyoon Cho
There is an increasing interest in the use of joint models for the analysis of longitudinal and survival data. While random effects models have been extensively studied, these models can be hard to implement and the fixed effect regression ...