Automated calibration for stability selection in penalised regression and graphical models [0.03%]
惩罚回归和图形模型中稳定性选择的自动化校准
Barbara Bodinier,Sarah Filippi,Therese Haugdahl Nøst et al.
Barbara Bodinier et al.
Stability selection represents an attractive approach to identify sparse sets of features jointly associated with an outcome in high-dimensional contexts. We introduce an automated calibration procedure via maximisation of an in-house stabi...
Estimating a brain network predictive of stress and genotype with supervised autoencoders [0.03%]
基于监督自编码器的预测压力和基因型的脑网络估算
Austin Talbot,David Dunson,Kafui Dzirasa et al.
Austin Talbot et al.
Targeted brain stimulation has the potential to treat mental illnesses. We develop an approach to help design protocols by identifying relevant multi-region electrical dynamics. Our approach models these dynamics as a superposition of laten...
Outcome trajectory estimation for optimal dynamic treatment regimes with repeated measures [0.03%]
重复测量的理想动态治疗方案的结果轨迹估计方法研究
Yuan Zhang,David M Vock,Megan E Patrick et al.
Yuan Zhang et al.
In recent sequential multiple assignment randomized trials, outcomes were assessed multiple times to evaluate longer-term impacts of the dynamic treatment regimes (DTRs). Q-learning requires a scalar response to identify the optimal DTR. In...
A Bayesian nonparametric analysis for zero-inflated multivariate count data with application to microbiome study [0.03%]
用于微生物组研究的零膨胀多元计数数据的贝叶斯非参数分析方法
Kurtis Shuler,Samuel Verbanic,Irene A Chen et al.
Kurtis Shuler et al.
High-throughput sequencing technology has enabled researchers to profile microbial communities from a variety of environments, but analysis of multivariate taxon count data remains challenging. We develop a Bayesian nonparametric (BNP) regr...
Seonjoo Lee,Jongwoo Choi,Zhiqian Fang et al.
Seonjoo Lee et al.
This paper considers canonical correlation analysis for two longitudinal variables that are possibly sampled at different time resolutions with irregular grids. We modeled trajectories of the multivariate variables using random effects and ...
A Bayesian feature allocation model for identifying cell subpopulations using CyTOF data [0.03%]
一种贝叶斯特征分配模型，用于使用CyTOF数据识别细胞亚群
Arthur Lui,Juhee Lee,Peter F Thall et al.
Arthur Lui et al.
A Bayesian feature allocation model (FAM) is presented for identifying cell subpopulations based on multiple samples of cell surface or intracellular marker expression level data obtained by cytometry by time of flight (CyTOF). Cell subpopu...
Andrew Gelman,Bob Carpenter
Andrew Gelman
When testing for a rare disease, prevalence estimates can be highly sensitive to uncertainty in the specificity and sensitivity of the test. Bayesian inference is a natural way to propagate these uncertainties, with hierarchical modelling c...
Andreas Kryger Jensen,Claus Thorn Ekstrøm
Andreas Kryger Jensen
News media often report that the trend of some public health outcome has changed. These statements are frequently based on longitudinal data, and the change in trend is typically found to have occurred at the most recent data collection tim...
Bora Jin,David B Dunson,Julia E Rager et al.
Bora Jin et al.
We aim to infer bioactivity of each chemical by assay endpoint combination, addressing sparsity of toxicology data. We propose a Bayesian hierarchical framework which borrows information across different chemicals and assay endpoints, facil...
Sparse tree-based clustering of microbiome data to characterize microbiome heterogeneity in pancreatic cancer [0.03%]
基于稀疏树的微生物组数据聚类以表征胰腺癌中的微生物组异质性
Yushu Shi,Liangliang Zhang,Kim-Anh Do et al.
Yushu Shi et al.
There is a keen interest in characterizing variation in the microbiome across cancer patients, given increasing evidence of its important role in determining treatment outcomes. Here our goal is to discover subgroups of patients with simila...