Gaussian and Mixed Graphical Models as (multi-) omics data analysis tools
M Altenbuchinger, A Weihs, J Quackenbush… - … et Biophysica Acta (BBA …, 2020 - Elsevier
Abstract Gaussian Graphical Models (GGMs) are tools to infer dependencies between
biological variables. Popular applications are the reconstruction of gene, protein, and …
biological variables. Popular applications are the reconstruction of gene, protein, and …
[HTML][HTML] Network-based approaches for modeling disease regulation and progression
Molecular interaction networks lay the foundation for studying how biological functions are
controlled by the complex interplay of genes and proteins. Investigating perturbed processes …
controlled by the complex interplay of genes and proteins. Investigating perturbed processes …
Back to the basics: Rethinking partial correlation network methodology
DR Williams, P Rast - British Journal of Mathematical and …, 2020 - Wiley Online Library
The Gaussian graphical model (GGM) is an increasingly popular technique used in
psychology to characterize relationships among observed variables. These relationships are …
psychology to characterize relationships among observed variables. These relationships are …
On nonregularized estimation of psychological networks
An important goal for psychological science is developing methods to characterize
relationships between variables. Customary approaches use structural equation models to …
relationships between variables. Customary approaches use structural equation models to …
Plasma p‐tau181 shows stronger network association to Alzheimer's disease dementia than neurofilament light and total tau
Introduction We examined the ability of plasma hyperphosphorylated tau (p‐tau) 181 to
detect cognitive impairment due to Alzheimer's disease (AD) independently and in …
detect cognitive impairment due to Alzheimer's disease (AD) independently and in …
Anomaly detection in mixed high-dimensional molecular data
L Buck, T Schmidt, M Feist, P Schwarzfischer… - …, 2023 - academic.oup.com
Motivation Mixed molecular data combines continuous and categorical features of the same
samples, such as OMICS profiles with genotypes, diagnoses, or patient sex. Like all high …
samples, such as OMICS profiles with genotypes, diagnoses, or patient sex. Like all high …
EnGRaiN: a supervised ensemble learning method for recovery of large-scale gene regulatory networks
M Aluru, H Shrivastava, SP Chockalingam… - …, 2022 - academic.oup.com
Motivation Reconstruction of genome-scale networks from gene expression data is an
actively studied problem. A wide range of methods that differ between the types of …
actively studied problem. A wide range of methods that differ between the types of …
[HTML][HTML] SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks
Gene co-expression network analysis is extremely useful in interpreting a complex
biological process. The recent droplet-based single-cell technology is able to generate …
biological process. The recent droplet-based single-cell technology is able to generate …
Transcriptomics of atopy and atopic asthma in white blood cells from children and adolescents
Early allergic sensitisation (atopy) is the first step in the development of allergic diseases
such as atopic asthma later in life. Genes and pathways associated with atopy and atopic …
such as atopic asthma later in life. Genes and pathways associated with atopy and atopic …
[HTML][HTML] A statistical test for differential network analysis based on inference of Gaussian graphical model
Differential network analysis investigates how the network of connected genes changes from
one condition to another and has become a prevalent tool to provide a deeper and more …
one condition to another and has become a prevalent tool to provide a deeper and more …