scholarly journals Gaussian graphical model‐based heterogeneity analysis via penalized fusion

Biometrics ◽  
2021 ◽  
Author(s):  
Mingyang Ren ◽  
Sanguo Zhang ◽  
Qingzhao Zhang ◽  
Shuangge Ma
Keyword(s):  
Graphical Model ◽  
Gaussian Graphical Model ◽  
Model Based ◽  
Heterogeneity Analysis

scholarly journals HeteroGGM: an R package for Gaussian graphical model-based heterogeneity analysis

2021 ◽  
Author(s):  
Mingyang Ren ◽  
Sanguo Zhang ◽  
Qingzhao Zhang ◽  
Shuangge Ma
Keyword(s):  
Graphical Model ◽  
R Package ◽  
Supplementary Information ◽  
Imaging Features ◽  
Gaussian Graphical Model ◽  
Variable Properties ◽  
Portable Software ◽  
Heterogeneity Analysis ◽  
Mean Variance ◽  
Graphical Presentation

Abstract Summary Heterogeneity is a hallmark of many complex human diseases, and unsupervised heterogeneity analysis has been extensively conducted using high-throughput molecular measurements and histopathological imaging features. ‘Classic’ heterogeneity analysis has been based on simple statistics such as mean, variance and correlation. Network-based analysis takes interconnections as well as individual variable properties into consideration and can be more informative. Several Gaussian graphical model (GGM)-based heterogeneity analysis techniques have been developed, but friendly and portable software is still lacking. To facilitate more extensive usage, we develop the R package HeteroGGM, which conducts GGM-based heterogeneity analysis using the advanced penaliztaion techniques, can provide informative summary and graphical presentation, and is efficient and friendly. Availabilityand implementation The package is available at https://CRAN.R-project.org/package=HeteroGGM. Supplementary information Supplementary data are available at Bioinformatics online.

A Probabilistic-Graphical-Model Based Approach for Representing Lineages in Uncertain Data

2011 ◽  
Vol 34 (10) ◽  
pp. 1897-1906 ◽  
Author(s):  
Kun YUE ◽  
Wei-Yi LIU ◽  
Yun-Lei ZHU ◽  
Wei ZHANG
Keyword(s):  
Graphical Model ◽  
Uncertain Data ◽  
Probabilistic Graphical Model ◽  
Model Based

A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints

2015 ◽  
Vol 43 (1) ◽  
pp. 267-281 ◽  
Author(s):  
Nikita Mishra ◽  
Huazhe Zhang ◽  
John D. Lafferty ◽  
Henry Hoffmann
Keyword(s):  
Graphical Model ◽  
Probabilistic Graphical Model ◽  
Performance Constraints ◽  
Model Based

Tensor-Based Gaussian Graphical Model

2021 ◽  
pp. 285-298
Author(s):  
Yipeng Liu ◽  
Jiani Liu ◽  
Zhen Long ◽  
Ce Zhu
Keyword(s):  
Graphical Model ◽  
Gaussian Graphical Model
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