Search results for "Graphical models"
showing 10 items of 17 documents
Genetic Network construction in CML gene expression profile data analysis
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. A central module is defined as a module of a GGM which contains genes that are defined differentially expressed.
Dynamic factorial graphical models for dynamic networks
2014
Dynamic networks models describe a growing number of important scientific processes, from cell biology and epidemiology to sociology and finance. Estimating dynamic networks from noisy time series data is a difficult task since the number of components involved in the system is very large. As a result, the number of parameters to be estimated is typically larger than the number of observations. However, a characteristic of many real life networks is that they are sparse. For example, the molec- ular structure of genes make interactions with other components a highly-structured and, therefore, a sparse process. Penalized Gaussian graphical models have been used to estimate sparse networks. H…
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…
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…
Dynamic Gaussian Graphical Models for Modelling Genomic Networks
2014
After sequencing the entire DNA for various organisms, the challenge has become understanding the functional interrelatedness of the genome. Only by understanding the pathways for various complex diseases can we begin to make sense of any type of treatment. Unfortunately, decyphering the genomic network structure is an enormous task. Even with a small number of genes the number of possible networks is very large. This problem becomes even more difficult, when we consider dynamical networks. We consider the problem of estimating a sparse dynamic Gaussian graphical model with \(L_1\) penalized maximum likelihood of structured precision matrix. The structure can consist of specific time dynami…
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…
Cyclic coordinate for penalized Gaussian graphical models with symmetry restriction
2014
In this paper we propose two efficient cyclic coordinate algorithms to estimate structured concentration matrix in penalized Gaussian graphical models. Symmetry restrictions on the concentration matrix are particularly useful to reduce the number of parameters to be estimated and to create specific structured graphs. The penalized Gaussian graphical models are suitable for high-dimensional data.
What should I do next? Using shared representations to solve interaction problems
2011
Studies on how “the social mind” works reveal that cognitive agents engaged in joint actions actively estimate and influence another’s cognitive variables and form shared representations with them. (How) do shared representations enhance coordination? In this paper, we provide a probabilistic model of joint action that emphasizes how shared representations help solving interaction problems. We focus on two aspects of the model. First, we discuss how shared representations permit to coordinate at the level of cognitive variables (beliefs, intentions, and actions) and determine a coherent unfolding of action execution and predictive processes in the brains of two agents. Second, we discuss th…
A computational method to estimate sparse multiple Gaussian graphical models
2012
In recent years several researchers have proposed the use of the Gaussian graphical model defined on a high dimensional setting to explore the dependence relationships between random variables. Standard methods, usually proposed in literature, are based on the use of a specific penalty function, such as the L1-penalty function. In this paper our aim is to estimate and compare two or more Gaussian graphical models defined in a high dimensional setting. In order to accomplish our aim, we propose a new computational method, based on glasso method, which lets us to extend the notion of p-value.
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…