Fast hybrid Bayesian integrative learning of multiple gene regulatory networks for type 1 diabetes [0.03%]
一种快速的混合贝叶斯综合学习方法在同一类型I糖尿病中同时学习多个基因调控网络
Bochao Jia,Faming Liang;TEDDY Study Group
Bochao Jia
Motivated by the study of the molecular mechanism underlying type 1 diabetes with gene expression data collected from both patients and healthy controls at multiple time points, we propose a hybrid Bayesian method for jointly estimating mul...
Qian Guan,Brian J Reich,Eric B Laber
Qian Guan
Malaria is an infectious disease affecting a large population across the world, and interventions need to be efficiently applied to reduce the burden of malaria. We develop a framework to help policy-makers decide how to allocate limited re...
Simultaneous differential network analysis and classification for matrix-variate data with application to brain connectivity [0.03%]
矩阵型数据的同步微分网络分析及分类方法及其在脑连接性研究中的应用
Hao Chen,Ying Guo,Yong He et al.
Hao Chen et al.
Growing evidence has shown that the brain connectivity network experiences alterations for complex diseases such as Alzheimer's disease (AD). Network comparison, also known as differential network analysis, is thus particularly powerful to ...
Kathrin Möllenhoff,Holger Dette,Frank Bretz
Kathrin Möllenhoff
Clinical trials often aim to compare two groups of patients for efficacy and/or toxicity depending on covariates such as dose. Examples include the comparison of populations from different geographic regions or age classes or, alternatively...
Structure-preserving integrated analysis for risk stratification with application to cancer staging [0.03%]
结构保持的综合分析在风险分层中的应用及癌症分期中的应用
Tianjie Wang,Rui Chen,Wenshuo Liu et al.
Tianjie Wang et al.
To provide appropriate and practical level of health care, it is critical to group patients into relatively few strata that have distinct prognosis. Such grouping or stratification is typically based on well-established risk factors and cli...
Information enhanced model selection for Gaussian graphical model with application to metabolomic data [0.03%]
基于代谢组数据的高斯图模型的扩充模型选择方法
Jie Zhou,Anne G Hoen,Susan Mcritchie et al.
Jie Zhou et al.
In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Our strategy includes two...
Bayesian biclustering for microbial metagenomic sequencing data via multinomial matrix factorization [0.03%]
基于多元矩阵分解的微生物元基因组测序数据双聚类的贝叶斯方法
Fangting Zhou,Kejun He,Qiwei Li et al.
Fangting Zhou et al.
High-throughput sequencing technology provides unprecedented opportunities to quantitatively explore human gut microbiome and its relation to diseases. Microbiome data are compositional, sparse, noisy, and heterogeneous, which pose serious ...
Hongyan Fang,Zeyu Zhang,Yinsheng Zhou et al.
Hongyan Fang et al.
The main challenge in cancer genomics is to distinguish the driver genes from passenger or neutral genes. Cancer genomes exhibit extensive mutational heterogeneity that no two genomes contain exactly the same somatic mutations. Such mutual ...
Dimension constraints improve hypothesis testing for large-scale, graph-associated, brain-image data [0.03%]
维度约束改善大型图谱关联脑影像数据的假设检验
Tien Vo,Akshay Mishra,Vamsi Ithapu et al.
Tien Vo et al.
For large-scale testing with graph-associated data, we present an empirical Bayes mixture technique to score local false-discovery rates (FDRs). Compared to procedures that ignore the graph, the proposed Graph-based Mixture Model (GraphMM) ...
Yei Eun Shin,Mitchell H Gail,Ruth M Pfeiffer
Yei Eun Shin
When validating a risk model in an independent cohort, some predictors may be missing for some subjects. Missingness can be unplanned or by design, as in case-cohort or nested case-control studies, in which some covariates are measured only...