Search results for "graphical models"
showing 7 items of 17 documents
Identifying modularity structure of a genetic network in gene expression profile data
2009
Aim of this paper is to define a new statistical framework to identify central modules in Gaussian Graphical Models (GGMs) estimated by gene expression data measured on a sample of patients with negative molecular response to Imatinib. Imanitib is a drug used to treat certain types of cancer that in many statistical studies has been reported to have a significant clinical effect on chronic myeloid leukemia (CML) in chronic phase as well as in blast crisis. For central module in a GGM we intend a module containing genes that are defined differently expressed.
Robustness of dynamic gene regulatory networks in Neisseria
2014
Gene regulatory networks are made of highly tuned, sparse and dynamical operations. We consider the case of the Neisseria meningitidis bacterium, a causative agent of life-threatening infections such as meningitis, and aim to infer a robust net- work of interactions across sixty proteins based on a detailed time course gene expres- sion study. We consider the problem of estimating a sparse dynamic Gaussian graphical model with L1 penalized maximum likelihood under a structured precision matrix. The structure can consist of specific time dynamics, known presence or absence of links in the graphical model or equality constraints on the parameters. The authors developed a new optimization algo…
Extending graphical models for applications: on covariates, missingness and normality
2021
The authors of the paper “Bayesian Graphical Models for Modern Biological Applications” have put forward an important framework for making graphical models more useful in applied settings. In this discussion paper, we give a number of suggestions for making this framework even more suitable for practical scenarios. Firstly, we show that an alternative and simplified definition of covariate might make the framework more manageable in high-dimensional settings. Secondly, we point out that the inclusion of missing variables is important for practical data analysis. Finally, we comment on the effect that the Gaussianity assumption has in identifying the underlying conditional independence graph…
The conditional censored graphical lasso estimator
2020
© 2020, Springer Science+Business Media, LLC, part of Springer Nature. In many applied fields, such as genomics, different types of data are collected on the same system, and it is not uncommon that some of these datasets are subject to censoring as a result of the measurement technologies used, such as data generated by polymerase chain reactions and flow cytometer. When the overall objective is that of network inference, at possibly different levels of a system, information coming from different sources and/or different steps of the analysis can be integrated into one model with the use of conditional graphical models. In this paper, we develop a doubly penalized inferential procedure for…
cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values
2023
Sparse graphical models have revolutionized multivariate inference. With the advent of high-dimensional multivariate data in many applied fields, these methods are able to detect a much lower-dimensional structure, often represented via a sparse conditional independence graph. There have been numerous extensions of such methods in the past decade. Many practical applications have additional covariates or suffer from missing or censored data. Despite the development of these extensions of sparse inference methods for graphical models, there have been so far no implementations for, e.g., conditional graphical models. Here we present the general-purpose package cglasso for estimating sparse co…
Machine learning for mortality analysis in patients with COVID-19
2020
This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis where the endpoint variable is the reason for hospital discharge (home or deceased). The different methods applied show the importance of variables such as age, O2 saturation at Emergency Rooms (ER), and whether the patient comes from a nursing home. In addition, biclustering is used to globally analyze the patient-drug dataset, extracting segments of patients. We highlight the validity of the classifiers developed to predict the mortality, reaching…
INFERRING GENE NETWORKS FROM MICROARRAY WITH GRAPHICAL MODELS
2013
ABSTRACT. Microarray technology allows to collect a large amount of genetic data, such as gene expression data. The activity of the genes are coordinate by a complex network that regulates their expressions controlling common functions, such as the formation of a transcriptional complex or the availability of a signalling pathway. Understanding this organization is crucial to explain normal cell physiology as well as to analyse complex pathological phenotypes. Graphical models are a class of statistical models that can be used to infer gene regulatory networks. In this paper, we examine a class of graphical models: the strongly decomposable graphical models for mixed variables. Among oth- e…