Results 31 to 40 of about 22,175,501 (301)
raxmlGUI 2.0: A graphical interface and toolkit for phylogenetic analyses using RAxML
Methods in Ecology and Evolution, 2020 raxmlGUI is a graphical user interface to RAxML, one of the most popular and widely used softwares for phylogenetic inference using maximum likelihood. Here we present raxmlGUI 2.0, a complete rewrite of the GUI which seamlessly integrates RAxML binaries
Daniel Edler +3 more
semanticscholar +1 more source
Detecting Community Evolution by Utilizing Individual Temporal Semantics in Social Networks
IEEE Access, 2023 Social networks are becoming increasingly popular and significant. One of the most distinctive features of these networks is their dynamic nature, which means that they change over time.
Feng Wang, Dingbo Hou, Hao Yan
doaj +1 more source
FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks
PLoS Comput. Biol., 2016 Biological networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single variables.
Ting Wang +8 more
semanticscholar +1 more source
Partition-Merge: Distributed Inference and Modularity Optimization
IEEE Access, 2021 This paper presents a novel meta-algorithm, Partition-Merge (PM), which takes existing centralized algorithms for graph computation and makes them distributed and faster. In a nutshell, PM divides the graph into small subgraphs using our novel randomized
Vincent Blondel +4 more
doaj +1 more source
Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model [PDF]
Journal of the American Statistical Association, 2013 We propose an asymptotically normal and efficient procedure to estimate every finite subgraph for covariate-adjusted Gaussian graphical model. As a consequence, a confidence interval as well as p-value can be obtained for each edge.
Mengjie Chen +3 more
semanticscholar +1 more source
Learning Graphical Model Parameters with Approximate Marginal Inference [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013 Likelihood-based learning of graphical models faces challenges of computational complexity and robustness to model misspecification. This paper studies methods that fit parameters directly to maximize a measure of the accuracy of predicted marginals ...
Justin Domke
semanticscholar +1 more source
High dimensional semiparametric latent graphical model for mixed data [PDF]
, 2014 We propose a semiparametric latent Gaussian copula model for modelling mixed multivariate data, which contain a combination of both continuous and binary variables. The model assumes that the observed binary variables are obtained by dichotomizing latent
Jianqing Fan, Han Liu, Y. Ning, H. Zou
semanticscholar +1 more source
Inferring Gene Dependency Networks from Genomic Longitudinal Data: a Functional Data Approach
Revstat Statistical Journal, 2006 A key aim of systems biology is to unravel the regulatory interactions among genes and gene products in a cell. Here we investigate a graphical model that treats the observed gene expression over time as realizations of random curves.
Rainer Opgen-Rhein , Korbinian Strimmer
doaj +1 more source
Annals of Medicine, 2023 Background Probabilistic graphical modelling (PGM) can be used to predict risk at the individual patient level and show multiple outcomes and exposures in a single model.Objective To develop PGM for the prediction of clinical outcome in patients with ...
Dong Ah Shin +6 more
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Gaussian graphical model estimation with false discovery rate control [PDF]
, 2013 This paper studies the estimation of high dimensional Gaussian graphical model (GGM). Typically, the existing methods depend on regularization techniques. As a result, it is necessary to choose the regularized parameter. However, the precise relationship
Weidong Liu
semanticscholar +1 more source