Compositional Graphical Lasso Resolves the Impact of Parasitic Infection on Gut Microbial Interaction Networks in a Zebrafish Model [0.03%]
组合图形Gasso解决了寄生虫感染对斑马鱼模型中肠道微生物相互作用网络的影响
Chuan Tian,Duo Jiang,Austin Hammer et al.
Chuan Tian et al.
Understanding how microbes interact with each other is key to revealing the underlying role that microorganisms play in the host or environment and to identifying microorganisms as an agent that can potentially alter the host or environment...
Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control [0.03%]
大规模高斯图形模型中带假发现率控制的变换学习
Sai Li,T Tony Cai,Hongzhe Li
Sai Li
Transfer learning for high-dimensional Gaussian graphical models (GGMs) is studied. The target GGM is estimated by incorporating the data from similar and related auxiliary studies, where the similarity between the target graph and each aux...
Jingfei Zhang,Yi Li
Jingfei Zhang
Though Gaussian graphical models have been widely used in many scientific fields, relatively limited progress has been made to link graph structures to external covariates. We propose a Gaussian graphical regression model, which regresses b...
Xiaowu Dai,Xiang Lyu,Lexin Li
Xiaowu Dai
Thanks to its fine balance between model flexibility and interpretability, the nonparametric additive model has been widely used, and variable selection for this type of model has been frequently studied. However, none of the existing solut...
Zhengling Qi,Jong-Shi Pang,Yufeng Liu
Zhengling Qi
With the emergence of precision medicine, estimating optimal individualized decision rules (IDRs) has attracted tremendous attention in many scientific areas. Most existing literature has focused on finding optimal IDRs that can maximize th...
Bayesian Double Feature Allocation for Phenotyping with Electronic Health Records [0.03%]
基于电子健康记录的表型分型的贝叶斯双特征分配法
Yang Ni,Peter Müller,Yuan Ji
Yang Ni
Electronic health records (EHR) provide opportunities for deeper understanding of human phenotypes - in our case, latent disease - based on statistical modeling. We propose a categorical matrix factorization method to infer latent diseases ...
Generalized Liquid Association Analysis for Multimodal Data Integration [0.03%]
多模态数据整合的广义液相关联分析方法研究
Lexin Li,Jing Zeng,Xin Zhang
Lexin Li
Multimodal data are now prevailing in scientific research. One of the central questions in multimodal integrative analysis is to understand how two data modalities associate and interact with each other given another modality or demographic...
Assessing disparities in Americans' exposure to PCBs and PBDEs based on NHANES pooled biomonitoring data [0.03%]
基于NHANES生物监测数据评估美国人群PCB和PBDE暴露差异
Yan Liu,Dewei Wang,Li Li et al.
Yan Liu et al.
The National Health and Nutrition Examination Survey (NHANES) has been continuously biomonitoring Americans' exposure to two families of harmful environmental chemicals: polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (P...
A Correlated Network Scale-up Model: Finding the Connection Between Subpopulations [0.03%]
相关网络规模升级模型：寻找亚群体之间的联系
Ian Laga,Le Bao,Xiaoyue Niu
Ian Laga
Aggregated relational data (ARD), formed from "How many X's do you know?" questions, is a powerful tool for learning important network characteristics with incomplete network data. Compared to traditional survey methods, ARD is attractive a...
Ian Laga,Le Bao,Xiaoyue Niu
Ian Laga
Estimating the size of hard-to-reach populations is an important problem for many fields. The Network Scale-up Method (NSUM) is a relatively new approach to estimate the size of these hard-to-reach populations by asking respondents the ques...