Thematische Bibliographien / Undirected Gaussian graphical model

Auswahl der wissenschaftlichen Literatur zum Thema „Undirected Gaussian graphical model“

Autor: Grafiati
Veröffentlicht am 23. Juni 2021
Zuletzt aktualisiert am 1. Februar 2022

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Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Buchteile
  4. Konferenzberichte

Zeitschriftenartikel zum Thema "Undirected Gaussian graphical model":

1

Jones, Beatrix, und Mike West. „Covariance decomposition in undirected Gaussian graphical models“. Biometrika 92, Nr. 4 (01.12.2005): 779–86. http://dx.doi.org/10.1093/biomet/92.4.779.

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2

Buntine, W. L. „Operations for Learning with Graphical Models“. Journal of Artificial Intelligence Research 2 (01.12.1994): 159–225. http://dx.doi.org/10.1613/jair.62.

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This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, andthe manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximizationalgorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing how some popular algorithms fall within the framework presented. The main original contributions here are the decompositiontechniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.
3

Zhao, Haitao, und Zhong-Hui Duan. „Cancer Genetic Network Inference Using Gaussian Graphical Models“. Bioinformatics and Biology Insights 13 (Januar 2019): 117793221983940. http://dx.doi.org/10.1177/1177932219839402.

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The Cancer Genome Atlas (TCGA) provides a rich resource that can be used to understand how genes interact in cancer cells and has collected RNA-Seq gene expression data for many types of human cancer. However, mining the data to uncover the hidden gene-interaction patterns remains a challenge. Gaussian graphical model (GGM) is often used to learn genetic networks because it defines an undirected graphical structure, revealing the conditional dependences of genes. In this study, we focus on inferring gene interactions in 15 specific types of human cancer using RNA-Seq expression data and GGM with graphical lasso. We take advantage of the corresponding Kyoto Encyclopedia of Genes and Genomes pathway maps to define the subsets of related genes. RNA-Seq expression levels of the subsets of genes in solid cancerous tumor and normal tissues were extracted from TCGA. The gene expression data sets were cleaned and formatted, and the genetic network corresponding to each cancer type was then inferred using GGM with graphical lasso. The inferred networks reveal stable conditional dependences among the genes at the expression level and confirm the essential roles played by the genes that encode proteins involved in the two key signaling pathway phosphoinositide 3-kinase (PI3K)/AKT/mTOR and Ras/Raf/MEK/ERK in human carcinogenesis. These stable dependences elucidate the expression level interactions among the genes that are implicated in many different human cancers. The inferred genetic networks were examined to further identify and characterize a collection of gene interactions that are unique to cancer. The cross-cancer genetic interactions revealed from our study provide another set of knowledge for cancer biologists to propose strong hypotheses, so further biological investigations can be conducted effectively.
4

Keune, Jessica, Christian Ohlwein und Andreas Hense. „Multivariate Probabilistic Analysis and Predictability of Medium-Range Ensemble Weather Forecasts“. Monthly Weather Review 142, Nr. 11 (24.10.2014): 4074–90. http://dx.doi.org/10.1175/mwr-d-14-00015.1.

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Abstract Ensemble weather forecasting has been operational for two decades now. However, the related uncertainty analysis in terms of probabilistic postprocessing still focuses on single variables, grid points, or stations. Inevitable dependencies in space and time and between variables are often ignored. To address this problem, two probabilistic postprocessing methods are presented, which are multivariate versions of Gaussian fit and kernel dressing, respectively. The multivariate case requires the estimation of a full rank, invertible covariance matrix. For this purpose, a Graphical Least Absolute Shrinkage and Selection Operators (GLASSO) estimator has been employed that is based on sparse undirected graphical models regularized by an L1 penalty term in order to parameterize the full rank inverse covariance. In all cases, the result is a multidimensional probability density. The forecasts used to test the approach are station forecasts of 2-m temperature and surface pressure from four main global ensemble prediction systems (EPS) with medium-range weather forecasts: the NCEP Global Ensemble Forecast System (GEFS), the Met Office Global and Regional Ensemble Prediction System (MOGREPS), the Canadian Meteorological Centre (CMC) Global Ensemble Prediction System (GEPS), and the ECMWF EPS. To evaluate the multivariate probabilistic postprocessing, especially the uncertainty estimates, common verification methods such as the analysis rank histogram and the continuous ranked probability score (CRPS) are applied. Furthermore, a multivariate extension of the CRPS, the energy score, allows for the verification of a complete medium-range forecast as well as for determining its predictability. It is shown that the predictability is similar for all of the examined ensemble prediction systems, whereas the GLASSO proved to be a useful tool for calibrating the commonly observed underdispersion of ensemble forecasts during the first few lead days by using information from the full covariance matrix.
5

Lotsi, Anani, und Ernst Wit. „Sparse Gaussian graphical mixture model“. Afrika Statistika 11, Nr. 2 (01.12.2016): 1041–59. http://dx.doi.org/10.16929/as/2016.1041.91.

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6

Yuan, Xiao-Tong, und Tong Zhang. „Partial Gaussian Graphical Model Estimation“. IEEE Transactions on Information Theory 60, Nr. 3 (März 2014): 1673–87. http://dx.doi.org/10.1109/tit.2013.2296784.

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7

Giudici, P. „Decomposable graphical Gaussian model determination“. Biometrika 86, Nr. 4 (01.12.1999): 785–801. http://dx.doi.org/10.1093/biomet/86.4.785.

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8

Zareifard, Hamid, Håvard Rue, Majid Jafari Khaledi und Finn Lindgren. „A skew Gaussian decomposable graphical model“. Journal of Multivariate Analysis 145 (März 2016): 58–72. http://dx.doi.org/10.1016/j.jmva.2015.08.011.

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9

Thomas, J., N. Ramakrishnan und C. Bailey-Kellogg. „Protein Design by Sampling an Undirected Graphical Model of Residue Constraints“. IEEE/ACM Transactions on Computational Biology and Bioinformatics 6, Nr. 3 (Juli 2009): 506–16. http://dx.doi.org/10.1109/tcbb.2008.124.

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Cheng, Lulu, Liang Shan und Inyoung Kim. „Multilevel Gaussian graphical model for multilevel networks“. Journal of Statistical Planning and Inference 190 (November 2017): 1–14. http://dx.doi.org/10.1016/j.jspi.2017.05.003.

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Zeitschriftenartikel Dissertationen

Dissertationen zum Thema "Undirected Gaussian graphical model":

1

Angelchev, Shiryaev Artem, und Johan Karlsson. „Estimating Dependence Structures with Gaussian Graphical Models : A Simulation Study in R“. Thesis, Umeå universitet, Statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184925.

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Graphical models are powerful tools when estimating complex dependence structures among large sets of data. This thesis restricts the scope to undirected Gaussian graphical models. An initial predefined sparse precision matrix was specified to generate multivariate normally distributed data. Utilizing the generated data, a simulation study was conducted reviewing accuracy, sensitivity and specificity of the estimated precision matrix. The graphical LASSO was applied using four different packages available in R with seven selection criteria's for estimating the tuning parameter. The findings are mostly in line with previous research. The graphical LASSO is generally faster and feasible in high dimensions, in contrast to stepwise model selection. A portion of the selection methods for estimating the optimal tuning parameter obtained the true network structure. The results provide an estimate of how well each model obtains the true, predefined dependence structure as featured in our simulation. As the simulated data used in this thesis is merely an approximation of real-world data, one should not take the results as the only aspect of consideration when choosing a model.
2

Kolar, Mladen. „Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems“. Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/229.

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Extracting knowledge and providing insights into complex mechanisms underlying noisy high-dimensional data sets is of utmost importance in many scientific domains. Statistical modeling has become ubiquitous in the analysis of high dimensional functional data in search of better understanding of cognition mechanisms, in the exploration of large-scale gene regulatory networks in hope of developing drugs for lethal diseases, and in prediction of volatility in stock market in hope of beating the market. Statistical analysis in these high-dimensional data sets is possible only if an estimation procedure exploits hidden structures underlying data. This thesis develops flexible estimation procedures with provable theoretical guarantees for uncovering unknown hidden structures underlying data generating process. Of particular interest are procedures that can be used on high dimensional data sets where the number of samples n is much smaller than the ambient dimension p. Learning in high-dimensions is difficult due to the curse of dimensionality, however, the special problem structure makes inference possible. Due to its importance for scientific discovery, we put emphasis on consistent structure recovery throughout the thesis. Particular focus is given to two important problems, semi-parametric estimation of networks and feature selection in multi-task learning.
3

Lin, Jiali. „Bayesian Multilevel-multiclass Graphical Model“. Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/101092.

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Gaussian graphical model has been a popular tool to investigate conditional dependency between random variables by estimating sparse precision matrices. Two problems have been discussed. One is to learn multiple Gaussian graphical models at multilevel from unknown classes. Another one is to select Gaussian process in semiparametric multi-kernel machine regression. The first problem is approached by Gaussian graphical model. In this project, I consider learning multiple connected graphs among multilevel variables from unknown classes. I esti- mate the classes of the observations from the mixture distributions by evaluating the Bayes factor and learn the network structures by fitting a novel neighborhood selection algorithm. This approach is able to identify the class membership and to reveal network structures for multilevel variables simultaneously. Unlike most existing methods that solve this problem by frequentist approaches, I assess an alternative to a novel hierarchical Bayesian approach to incorporate prior knowledge. The second problem focuses on the analysis of correlated high-dimensional data which has been useful in many applications. In this work, I consider a problem of detecting signals with a semiparametric regression model which can study the effects of fixed covariates (e.g. clinical variables) and sets of elements (e.g. pathways of genes). I model the unknown high-dimension functions of multi-sets via multi-Gaussian kernel machines to consider the possibility that elements within the same set interact with each other. Hence, my variable selection can be considered as Gaussian process selection. I develop my Gaussian process selection under the Bayesian variable selection framework.
Doctor of Philosophy
4

Lartigue, Thomas. „Mixtures of Gaussian Graphical Models with Constraints Gaussian Graphical Model exploration and selection in high dimension low sample size setting“. Thesis, Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAX034.

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La description des co-variations entre plusieurs variables aléatoires observées est un problème délicat. Les réseaux de dépendance sont des outils populaires qui décrivent les relations entre les variables par la présence ou l’absence d’arêtes entre les nœuds d’un graphe. En particulier, les graphes de corrélations conditionnelles sont utilisés pour représenter les corrélations “directes” entre les nœuds du graphe. Ils sont souvent étudiés sous l’hypothèse gaussienne et sont donc appelés “modèles graphiques gaussiens” (GGM). Un seul réseau peut être utilisé pour représenter les tendances globales identifiées dans un échantillon de données. Toutefois, lorsque les données observées sont échantillonnées à partir d’une population hétérogène, il existe alors différentes sous-populations qui doivent toutes être décrites par leurs propres graphes. De plus, si les labels des sous populations (ou “classes”) ne sont pas disponibles, des approches non supervisées doivent être mises en œuvre afin d’identifier correctement les classes et de décrire chacune d’entre elles avec son propre graphe. Dans ce travail, nous abordons le problème relativement nouveau de l’estimation hiérarchique des GGM pour des populations hétérogènes non labellisées. Nous explorons plusieurs axes clés pour améliorer l’estimation des paramètres du modèle ainsi que l’identification non supervisee des sous-populations. ´ Notre objectif est de s’assurer que les graphes de corrélations conditionnelles inférés sont aussi pertinents et interprétables que possible. Premièrement - dans le cas d’une population simple et homogène - nous développons une méthode composite qui combine les forces des deux principaux paradigmes de l’état de l’art afin d’en corriger les faiblesses. Pour le cas hétérogène non labellisé, nous proposons d’estimer un mélange de GGM avec un algorithme espérance-maximisation (EM). Afin d’améliorer les solutions de cet algorithme EM, et d’éviter de tomber dans des extrema locaux sous-optimaux quand les données sont en grande dimension, nous introduisons une version tempérée de cet algorithme EM, que nous étudions théoriquement et empiriquement. Enfin, nous améliorons le clustering de l’EM en prenant en compte l’effet que des cofacteurs externes peuvent avoir sur la position des données observées dans leur espace
Describing the co-variations between several observed random variables is a delicate problem. Dependency networks are popular tools that depict the relations between variables through the presence or absence of edges between the nodes of a graph. In particular, conditional correlation graphs are used to represent the “direct” correlations between nodes of the graph. They are often studied under the Gaussian assumption and consequently referred to as “Gaussian Graphical Models” (GGM). A single network can be used to represent the overall tendencies identified within a data sample. However, when the observed data is sampled from a heterogeneous population, then there exist different sub-populations that all need to be described through their own graphs. What is more, if the sub-population (or “class”) labels are not available, unsupervised approaches must be implemented in order to correctly identify the classes and describe each of them with its own graph. In this work, we tackle the fairly new problem of Hierarchical GGM estimation for unlabelled heterogeneous populations. We explore several key axes to improve the estimation of the model parameters as well as the unsupervised identification of the sub-populations. Our goal is to ensure that the inferred conditional correlation graphs are as relevant and interpretable as possible. First - in the simple, homogeneous population case - we develop a composite method that combines the strengths of the two main state of the art paradigms to correct their weaknesses. For the unlabelled heterogeneous case, we propose to estimate a Mixture of GGM with an Expectation Maximisation (EM) algorithm. In order to improve the solutions of this EM algorithm, and avoid falling for sub-optimal local extrema in high dimension, we introduce a tempered version of this EM algorithm, that we study theoretically and empirically. Finally, we improve the clustering of the EM by taking into consideration the effect of external co-features on the position in space of the observed data
5

Lai, Wai Lok M. Eng Massachusetts Institute of Technology. „A probabilistic graphical model based data compression architecture for Gaussian sources“. Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/117322.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 107-108).
Data is compressible because of inherent redundancies in the data, mathematically expressed as correlation structures. A data compression algorithm uses the knowledge of these structures to map the original data to a different encoding. The two aspects of data compression, source modeling, ie. using knowledge about the source, and coding, ie. assigning an output sequence of symbols to each output, are not inherently related, but most existing algorithms mix the two and treat the two as one. This work builds on recent research on model-code separation compression architectures to extend this concept into the domain of lossy compression of continuous sources, in particular, Gaussian sources. To our knowledge, this is the first attempt with using with sparse linear coding and discrete-continuous hybrid graphical model decoding for compressing continuous sources. With the flexibility afforded by the modularity of the architecture, we show that the proposed system is free from many inadequacies of existing algorithms, at the same time achieving competitive compression rates. Moreover, the modularity allows for many architectural extensions, with capabilities unimaginable for existing algorithms, including refining of source model after compression, robustness to data corruption, seamless interface with source model parameter learning, and joint homomorphic encryption-compression. This work, meant to be an exploration in a new direction in data compression, is at the intersection of Electrical Engineering and Computer Science, tying together the disciplines of information theory, digital communication, data compression, machine learning, and cryptography.
by Wai Lok Lai.
M. Eng.
6

Shan, Liang. „Joint Gaussian Graphical Model for multi-class and multi-level data“. Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/81412.

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Gaussian graphical model has been a popular tool to investigate conditional dependency between random variables by estimating sparse precision matrices. The estimated precision matrices could be mapped into networks for visualization. For related but different classes, jointly estimating networks by taking advantage of common structure across classes can help us better estimate conditional dependencies among variables. Furthermore, there may exist multilevel structure among variables; some variables are considered as higher level variables and others are nested in these higher level variables, which are called lower level variables. In this dissertation, we made several contributions to the area of joint estimation of Gaussian graphical models across heterogeneous classes: the first is to propose a joint estimation method for estimating Gaussian graphical models across unbalanced multi-classes, whereas the second considers multilevel variable information during the joint estimation procedure and simultaneously estimates higher level network and lower level network. For the first project, we consider the problem of jointly estimating Gaussian graphical models across unbalanced multi-class. Most existing methods require equal or similar sample size among classes. However, many real applications do not have similar sample sizes. Hence, in this dissertation, we propose the joint adaptive graphical lasso, a weighted L1 penalized approach, for unbalanced multi-class problems. Our joint adaptive graphical lasso approach combines information across classes so that their common characteristics can be shared during the estimation process. We also introduce regularization into the adaptive term so that the unbalancedness of data is taken into account. Simulation studies show that our approach performs better than existing methods in terms of false positive rate, accuracy, Mathews correlation coefficient, and false discovery rate. We demonstrate the advantage of our approach using liver cancer data set. For the second one, we propose a method to jointly estimate the multilevel Gaussian graphical models across multiple classes. Currently, methods are still limited to investigate a single level conditional dependency structure when there exists the multilevel structure among variables. Due to the fact that higher level variables may work together to accomplish certain tasks, simultaneously exploring conditional dependency structures among higher level variables and among lower level variables are of our main interest. Given multilevel data from heterogeneous classes, our method assures that common structures in terms of the multilevel conditional dependency are shared during the estimation procedure, yet unique structures for each class are retained as well. Our proposed approach is achieved by first introducing a higher level variable factor within a class, and then common factors across classes. The performance of our approach is evaluated on several simulated networks. We also demonstrate the advantage of our approach using breast cancer patient data.
Ph. D.
7

Zhao, Haitao. „Learning Genetic Networks Using Gaussian Graphical Model and Large-Scale Gene Expression Data“. University of Akron / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=akron1595682639738664.

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8

Kontos, Kevin. „Gaussian graphical model selection for gene regulatory network reverse engineering and function prediction“. Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210301.

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One of the most important and challenging ``knowledge extraction' tasks in bioinformatics is the reverse engineering of gene regulatory networks (GRNs) from DNA microarray gene expression data. Indeed, as a result of the development of high-throughput data-collection techniques, biology is experiencing a data flood phenomenon that pushes biologists toward a new view of biology--systems biology--that aims at system-level understanding of biological systems.

Unfortunately, even for small model organisms such as the yeast Saccharomyces cerevisiae, the number p of genes is much larger than the number n of expression data samples. The dimensionality issue induced by this ``small n, large p' data setting renders standard statistical learning methods inadequate. Restricting the complexity of the models enables to deal with this serious impediment. Indeed, by introducing (a priori undesirable) bias in the model selection procedure, one reduces the variance of the selected model thereby increasing its accuracy.

Gaussian graphical models (GGMs) have proven to be a very powerful formalism to infer GRNs from expression data. Standard GGM selection techniques can unfortunately not be used in the ``small n, large p' data setting. One way to overcome this issue is to resort to regularization. In particular, shrinkage estimators of the covariance matrix--required to infer GGMs--have proven to be very effective. Our first contribution consists in a new shrinkage estimator that improves upon existing ones through the use of a Monte Carlo (parametric bootstrap) procedure.

Another approach to GGM selection in the ``small n, large p' data setting consists in reverse engineering limited-order partial correlation graphs (q-partial correlation graphs) to approximate GGMs. Our second contribution consists in an inference algorithm, the q-nested procedure, that builds a sequence of nested q-partial correlation graphs to take advantage of the smaller order graphs' topology to infer higher order graphs. This allows us to significantly speed up the inference of such graphs and to avoid problems related to multiple testing. Consequently, we are able to consider higher order graphs, thereby increasing the accuracy of the inferred graphs.

Another important challenge in bioinformatics is the prediction of gene function. An example of such a prediction task is the identification of genes that are targets of the nitrogen catabolite repression (NCR) selection mechanism in the yeast Saccharomyces cerevisiae. The study of model organisms such as Saccharomyces cerevisiae is indispensable for the understanding of more complex organisms. Our third contribution consists in extending the standard two-class classification approach by enriching the set of variables and comparing several feature selection techniques and classification algorithms.

Finally, our fourth contribution formulates the prediction of NCR target genes as a network inference task. We use GGM selection to infer multivariate dependencies between genes, and, starting from a set of genes known to be sensitive to NCR, we classify the remaining genes. We hence avoid problems related to the choice of a negative training set and take advantage of the robustness of GGM selection techniques in the ``small n, large p' data setting.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished

9

Pacini, Clare. „Inferring condition specific regulatory networks with small sample sizes : a case study in Bacillus subtilis and infection of Mus musculus by the parasite Toxoplasma gondii“. Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/269711.

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Modelling interactions between genes and their regulators is fundamental to understanding how, for example a disease progresses, or the impact of inserting a synthetic circuit into a cell. We use an existing method to infer regulatory networks under multiple conditions: the Joint Graphical Lasso (JGL), a shrinkage based Gaussian graphical model. We apply this method to two data sets: one, a publicly available set of microarray experiments perturbing the gram-positive bacteria Bacillus subtilis under multiple experimental conditions; the second, a set of RNA-seq samples of Mouse (Mus musculus) embryonic fibroblasts (MEFs) infected with different strains of the parasite Toxoplasma gondii. In both cases we infer a subset of the regulatory networks using relatively small sample sizes. For the Bacillus subtilis analysis we focused on the use of these regulatory networks in synthetic biology and found examples of transcriptional units active only under a subset of conditions, this information can be useful when designing circuits to have condition dependent behaviour. We developed methods for large network decomposition that made use of the condition information and showed a greater specificity of identifying single transcriptional units from the larger network using our method. Through annotating these results with known information we were able to identify novel connections and found supporting evidence for a selection of these from publicly available experimental results. Biological data collection is typically expensive and due to the relatively small sample sizes of our MEF data set we developed a novel empirical Bayes method for reducing the false discovery rate when estimating block diagonal covariance matrices. Using these methods we were able to infer regulatory networks for the host infected with either the ME49 or RH strain of the parasite. This enabled the identification of known and novel regulatory mechanisms. The Toxoplasma gondii parasite has shown to subvert host function using similar mechanisms as cancers and through our analysis we were able to identify genes, networks and ontologies associated with cancer, including connections that have not previously been associated with T. gondii infection. Finally a Shiny application was developed as an online resource giving access to the Bacillus subtilis inferred networks with interactive methods for exploring the networks including expansion of sub networks and large network decomposition.
10

Frot, Benjamin. „Graphical model selection for Gaussian conditional random fields in the presence of latent variables : theory and application to genetics“. Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:0a6799ed-fca1-48b2-89cd-ad6f2c0439af.

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The task of performing graphical model selection arises in many applications in science and engineering. The field of application of interest in this thesis relates to the needs of datasets that include genetic and multivariate phenotypic data. There are several factors that make this problem particularly challenging: some of the relevant variables might not be observed, high-dimensionality might cause identifiability issues and, finally, it might be preferable to learn the model over a subset of the collection while conditioning on the rest of the variables, e.g. genetic variants. We suggest addressing these problems by learning a conditional Gaussian graphical model, while accounting for latent variables. Building on recent advances in this field, we decompose the parameters of a conditional Markov random field into the sum of a sparse and a low-rank matrix. We derive convergence bounds for this novel estimator, show that it is well-behaved in the high-dimensional regime and describe algorithms that can be used when the number of variables is in the thousands. Through simulations, we illustrate the conditions required for identifiability and show that this approach is consistent in a wider range of settings. In order to show the practical implications of our work, we apply our method to two real datasets and devise a metric that makes use of an independent source of information to assess the biological relevance of the estimates. In our first application, we use the proposed approach to model the levels of 39 metabolic traits conditional on hundreds of genetic variants, in two independent cohorts. We find our results to be better replicated across cohorts than the ones obtained with other methods. In our second application, we look at a high-dimensional gene expression dataset. We find that our method is capable of retrieving as many biologically relevant gene-gene interactions as other methods while retrieving fewer irrelevant interaction.
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Buchteile zum Thema "Undirected Gaussian graphical model":

1

Zhang, Zhong-Yuan. „Graphical Gaussian Model“. In Encyclopedia of Systems Biology, 867–68. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_401.

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Liu, Yipeng, Jiani Liu, Zhen Long und Ce Zhu. „Tensor-Based Gaussian Graphical Model“. In Tensor Computation for Data Analysis, 285–98. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74386-4_12.

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Chen, Yarui, Congcong Xiong und Hailin Xie. „Gaussian Message Propagation in d-order Neighborhood for Gaussian Graphical Model“. In Advances in Neural Networks – ISNN 2013, 539–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39065-4_65.

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Verma, Krishnakant, und Mukesh A. Zaveri. „A Gaussian Graphical Model Based Approach for Image Inpainting“. In Advances in Intelligent and Soft Computing, 159–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30157-5_17.

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5

Grechikhin, Ivan S., und Valery A. Kalyagin. „Comparison of Statistical Procedures for Gaussian Graphical Model Selection“. In Computational Aspects and Applications in Large-Scale Networks, 269–79. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96247-4_19.

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Ng, Bernard, Gaël Varoquaux, Jean Baptiste Poline und Bertrand Thirion. „A Novel Sparse Group Gaussian Graphical Model for Functional Connectivity Estimation“. In Lecture Notes in Computer Science, 256–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38868-2_22.

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7

Kalyagin, Valery A., Alexander P. Koldanov, Petr A. Koldanov und Panos M. Pardalos. „Optimality of Multiple Decision Statistical Procedure for Gaussian Graphical Model Selection“. In Lecture Notes in Computer Science, 304–8. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05348-2_26.

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8

Zhu, Zhiyuan, Zonglei Zhen und Xia Wu. „A Novel Sparse Overlapping Modularized Gaussian Graphical Model for Functional Connectivity Estimation“. In Lecture Notes in Computer Science, 304–15. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20351-1_23.

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9

Ng, Bernard, Anna-Clare Milazzo und Andre Altmann. „Node-Based Gaussian Graphical Model for Identifying Discriminative Brain Regions from Connectivity Graphs“. In Machine Learning in Medical Imaging, 44–51. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24888-2_6.

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10

Chiquet, Julien, Guillem Rigaill und Martina Sundqvist. „A Multiattribute Gaussian Graphical Model for Inferring Multiscale Regulatory Networks: An Application in Breast Cancer“. In Methods in Molecular Biology, 143–60. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8882-2_6.

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Konferenzberichte zum Thema "Undirected Gaussian graphical model":

1

Tugnait, Jitendra K. „Graphical Lasso for High-dimensional Complex Gaussian Graphical Model Selection“. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682867.

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2

Tugnait, Jitendra K. „On Sparse Complex Gaussian Graphical Model Selection“. In 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2019. http://dx.doi.org/10.1109/mlsp.2019.8918691.

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3

Tugnait, Jitendra K. „Scad-Penalized Complex Gaussian Graphical Model Selection“. In 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2020. http://dx.doi.org/10.1109/mlsp49062.2020.9231821.

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4

Bag, Abhishek, Bandana Barman und Goutam Saha. „Finding Genetic network using Graphical Gaussian Model“. In 2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems (ICIIS). IEEE, 2008. http://dx.doi.org/10.1109/iciinfs.2008.4798365.

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5

Dauwels, Justin, Hang Yu, Shiyan Xu und Xueou Wang. „Copula Gaussian graphical model for discrete data“. In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638874.

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6

Uda, Shinsuke, und Hiroyuki Kubota. „Sparse Gaussian graphical model with missing values“. In 2017 21st Conference of Open Innovations Association (FRUCT). IEEE, 2017. http://dx.doi.org/10.23919/fruct.2017.8250201.

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7

Dasarathy, Gautam. „Gaussian Graphical Model Selection from Size Constrained Measurements“. In 2019 IEEE International Symposium on Information Theory (ISIT). IEEE, 2019. http://dx.doi.org/10.1109/isit.2019.8849299.

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8

Yao, Tianyi, und Genevera I. Allen. „Clustered Gaussian Graphical Model Via Symmetric Convex Clustering“. In 2019 IEEE Data Science Workshop (DSW). IEEE, 2019. http://dx.doi.org/10.1109/dsw.2019.8755774.

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9

Takai, Keiji. „Exploration of Dependencies among Sections in a Supermarket Using a Tree-Structured Undirected Graphical Model“. In 2012 IEEE 12th International Conference on Data Mining Workshops. IEEE, 2012. http://dx.doi.org/10.1109/icdmw.2012.105.

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10

Phan, Dzung T., Tsuyoshi Ide, Jayant Kalagnanam, Matt Menickelly und Katya Scheinberg. „A Novel l0-Constrained Gaussian Graphical Model for Anomaly Localization“. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2017. http://dx.doi.org/10.1109/icdmw.2017.115.

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