Evaluating Functional Autocorrelation within Spatially Distributed Neural Processing Networks [0.03%]
评估空间分布神经处理网络内的功能自相关性
Gordana Derado,F Dubois Bowman,Timothy D Ely et al.
Gordana Derado et al.
Data-driven statistical approaches, such as cluster analysis or independent component analysis, applied to in vivo functional neuroimaging data help to identify neural processing networks that exhibit similar task-related or restingstate pa...
Detecting essential and removable interactions in genome-wide association studies [0.03%]
全基因组关联研究中可移除和必需的SNP相互作用识别方法
Chengqing Wu,Hong Zhang,Xiangtao Liu et al.
Chengqing Wu et al.
Detection of disease gene interaction effects among the enormous array of single nucleotide polymorphism (SNP) combinations represents the next frontier in genome-wide association (GWA) studies. Here we propose a novel strategy on the basis...
A latent model approach to define event onset time in the presence of measurement error [0.03%]
一种定义事件起始时间的潜在模型方法及其在测量误差情况下的应用
Peng Huang,Ming-Hui Chen,Debajyoti Sinha
Peng Huang
For progressive diseases, it is often not so straightforward to define an onset time of certain disease condition due to disease fluctuation and clinical measurement variation. When a disease onset is claimed through the first presence of s...
Within-Cluster Resampling for Analysis of Family Data: Ready for Prime-Time? [0.03%]
家庭数据的簇内重抽样分析：准备就绪了吗？
Hemant K Tiwari,Amit Patki,David B Allison
Hemant K Tiwari
Hoffman et al. [1] proposed an elegant resampling method for analyzing clustered binary data. The focus of their paper was to perform association tests on clustered binary data using within-cluster-resampling (WCR) method. Follmann et al. [...
A weighted cluster kernel PCA prediction model for multi-subject brain imaging data [0.03%]
一种基于加权聚类核PCA的多被试脑影像数据预测模型
Ying Guo
Ying Guo
Brain imaging data have shown great promise as a useful predictor for psychiatric conditions, cognitive functions and many other neural-related outcomes. Development of prediction models based on imaging data is challenging due to the high ...
Patrick Breheny,Jian Huang
Patrick Breheny
In many applications, covariates possess a grouping structure that can be incorporated into the analysis to select important groups as well as important members of those groups. This work focuses on the incorporation of grouping structure i...
Booil Jo,Chen-Pin Wang,Nicholas S Ialongo
Booil Jo
In longitudinal studies, outcome trajectories can provide important information about substantively and clinically meaningful underlying subpopulations who may also respond differently to treatments or interventions. Growth mixture analysis...
Support Vector Machines with Disease-gene-centric Network Penalty for High Dimensional Microarray Data [0.03%]
基于疾病基因中心网络惩罚的支持向量机方法及其在高维微阵列数据中的应用
Yanni Zhu,Wei Pan,Xiaotong Shen
Yanni Zhu
With the availability of genetic pathways or networks and accumulating knowledge on genes with variants predisposing to diseases (disease genes), we propose a disease-gene-centric support vector machine (DGC-SVM) that directly incorporates ...
A mixed ordinal location scale model for analysis of Ecological Momentary Assessment (EMA) data [0.03%]
生态瞬时评估数据的混合序次位置尺度模型分析
Donald Hedeker,Hakan Demirtas,Robin J Mermelstein
Donald Hedeker
Mixed-effects logistic regression models are described for analysis of longitudinal ordinal outcomes, where observations are observed clustered within subjects. Random effects are included in the model to account for the correlation of the ...
Partitioning of Functional Data for Understanding Heterogeneity in Psychiatric Conditions [0.03%]
功能性数据划分以理解精神病条件的异质性
Eva Petkova,Thaddeus Tarpey
Eva Petkova