Evaluating Missing Data Imputation Strategies for Environmental Mixture Models: A Simulation Study and Applied Analysis [0.03%]
环境混合模型中评估缺失数据插补策略：一项模拟研究和应用分析
Yvonne S Boafo,Sayed Mostafa,Emmanuel Obeng-Gyasi
Yvonne S Boafo
Missing data are common in environmental mixture studies and can bias inference if not properly addressed. This study evaluates how different imputation strategies influence variable selection performance in six mixture modeling frameworks:...
Bayesian Covariate-Dependent Circadian Modeling of Rest-Activity Rhythms [0.03%]
基于贝叶斯方法的昼夜作息规律协变量依赖模型研究
Beniamino Hadj-Amar,Vaishnav Krishnan,Marina Vannucci
Beniamino Hadj-Amar
We propose a Bayesian covariate-dependent anti-logistic circadian model for analyzing activity data collected via wrist-worn wearable devices. The proposed approach integrates covariates into the modeling of the amplitude and phase paramete...
Brian Schetzsle,Jaylen Lee,Aaron Bornstein et al.
Brian Schetzsle et al.
Functional connectivity, the study of coordination between distinct brain regions, is a key focus in neuroscience. The Psychophysiological Interaction (PPI) model, commonly used to infer task-dependent functional connectivity, is limited by...
Keiland W Cooper,Babak Shahbaba,Norbert J Fortin
Keiland W Cooper
The study of the brain is a compelling example of the power of convergent science. Over the last few decades, advances in neuroscience techniques and experimentation, as well as in data science tools to analyze the resulting data, have dram...
A Bayesian Integrative Mixed Modeling Framework for Analysis of the Multi-Site Adolescent Brain and Cognitive Development Study [0.03%]
一种用于分析多中心青少年大脑和认知发展研究的贝叶斯综合混合建模框架
Aidan Neher,Apostolos Stamenos,Mark Fiecas et al.
Aidan Neher et al.
Integrating high-dimensional, heterogeneous data from multi-site cohort studies with complex hierarchical structures poses significant variable selection and prediction challenges. We extend the Bayesian Integrative Analysis and Prediction ...
Enhancing Health Research with Machine Learning: Practical Case Studies Using the All of Us Researcher Workbench [0.03%]
利用机器学习增强健康研究：使用All of Us researcher workbench的实用案例研究
Jonathan R Holt,Stefanee Tillman,Javan Carter et al.
Jonathan R Holt et al.
Machine learning is revolutionizing health research by enabling scalable analysis across complex datasets. The All of Us Research Program offers unprecedented access to a wealth of health data. To harness this potential, researchers must na...
TIME-VARYING ℓ 0 OPTIMIZATION FOR SPIKE INFERENCE FROM MULTI-TRIAL CALCIUM RECORDINGS [0.03%]
基于多试次钙成像数据的脉冲时间varyingℓ0优化方法用于神经元放电inferrence
Tong Shen,Mingyu DU,Kevin Johnston et al.
Tong Shen et al.
Optical imaging of genetically encoded calcium indicators is a powerful tool to record the activity of a large number of neurons simultaneously over a long period of time from freely behaving animals. However, determining the exact time at ...
Assessment of Glioblastoma Multiforme Tumor Heterogeneity via MRI-derived Shape and Intensity Features [0.03%]
基于磁共振成像的形状和强度特征评估多形性胶质母细胞瘤肿瘤异质性
Yi Tang Chen,Sebastian Kurtek
Yi Tang Chen
We use a geometric approach to jointly characterize tumor shape and intensity along the tumor contour, as captured in magnetic resonance images, in the context of glioblastoma multiforme. Key properties of the proposed shape+intensity repre...
Adaptive Sequential Singular Spectrum Analysis: Effective Signal Extraction with Application to Heart Rate Signals Related to E-cigarette Use [0.03%]
自适应序列谱域分析及其在电子烟使用者心跳信号提取中的应用研究
James J Yang,Anne Buu
James J Yang
The Singular Spectrum Analysis (SSA) is a useful tool for extracting signals from noisy time series. However, the structural insights provided by SSA are significantly influenced by the choice of window length. While the conventional approa...
Biclustering Multivariate Longitudinal Data with Application to Recovery Trajectories of White Matter After Sport-Related Concussion [0.03%]
体育相关脑震荡后纵向多变量的双向聚类及其在白质恢复轨迹中的应用
Caleb Weaver,Luo Xiao,Qiuting Wen et al.
Caleb Weaver et al.
Biclustering is the task of simultaneously clustering the samples and features of a data set. In doing so, subsets of samples that exhibit similar behaviors across subsets of features can be identified. Motivated by a longitudinal diffusion...