HMM for discovering decision-making dynamics using reinforcement learning experiments [0.03%]
基于强化学习实验的决策过程动态变化的隐马尔科夫模型研究
Xingche Guo,Donglin Zeng,Yuanjia Wang
Xingche Guo
Major depressive disorder (MDD), a leading cause of years of life lived with disability, presents challenges in diagnosis and treatment due to its complex and heterogeneous nature. Emerging evidence indicates that reward processing abnormal...
Correction [0.03%]
改正
Published Erratum
Biostatistics (Oxford, England). 2024 Aug 26:kxae029. DOI:10.1093/biostatistics/kxae029 2024
Ying Huang,Dean Follmann
Ying Huang
Immune response decays over time, and vaccine-induced protection often wanes. Understanding how vaccine efficacy changes over time is critical to guiding the development and application of vaccines in preventing infectious diseases. The obj...
Danni Tu,Julia Wrobel,Theodore D Satterthwaite et al.
Danni Tu et al.
In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coe...
Stochastic EM algorithm for partially observed stochastic epidemics with individual heterogeneity [0.03%]
具有个体异质性的部分观测随机流行病的随机EM算法
Fan Bu,Allison E Aiello,Alexander Volfovsky et al.
Fan Bu et al.
We develop a stochastic epidemic model progressing over dynamic networks, where infection rates are heterogeneous and may vary with individual-level covariates. The joint dynamics are modeled as a continuous-time Markov chain such that dise...
Adaptive Gaussian Markov random fields for child mortality estimation [0.03%]
自适应高斯马尔可夫随机场在儿童生存率估计中的应用
Serge Aleshin-Guendel,Jon Wakefield
Serge Aleshin-Guendel
The under-5 mortality rate (U5MR), a critical health indicator, is typically estimated from household surveys in lower and middle income countries. Spatio-temporal disaggregation of household survey data can lead to highly variable estimate...
Direct estimation and inference of higher-level correlations from lower-level measurements with applications in gene-pathway and proteomics studies [0.03%]
基于低层次测量直接估计和推断高层次相关性的方法及其在基因通路与蛋白质组学研究中的应用
Yue Wang,Haoran Shi
Yue Wang
This paper tackles the challenge of estimating correlations between higher-level biological variables (e.g. proteins and gene pathways) when only lower-level measurements are directly observed (e.g. peptides and individual genes). Existing ...
Estimating causal effects for binary outcomes using per-decision inverse probability weighting [0.03%]
基于每次决策的逆概率加权估算二元结果的因果效应
Yihan Bao,Lauren Bell,Elizabeth Williamson et al.
Yihan Bao et al.
Micro-randomized trials are commonly conducted for optimizing mobile health interventions such as push notifications for behavior change. In analyzing such trials, causal excursion effects are often of primary interest, and their estimation...
Incorporating prior information in gene expression network-based cancer heterogeneity analysis [0.03%]
在基因表达网络基础上基于癌症异质性分析中引入先验信息
Rong Li,Shaodong Xu,Yang Li et al.
Rong Li et al.
Cancer is molecularly heterogeneous, with seemingly similar patients having different molecular landscapes and accordingly different clinical behaviors. In recent studies, gene expression networks have been shown as more effective/informati...
Neuroimaging meta regression for coordinate based meta analysis data with a spatial model [0.03%]
基于坐标的元分析的神经影像学元回归及空间模型方法研究
Yifan Yu,Rosario Pintos Lobo,Michael Cody Riedel et al.
Yifan Yu et al.
Coordinate-based meta-analysis combines evidence from a collection of neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally efficient approach with good statistical inter...