Dealing with data of a specific temporal or spatial structure is well established in blind source separation. However, in biology one often faces more complex network structures. The recently published GraDe-algorithm addresses such structures; it separates sources with respect to a given network in an analytical manner. We formulate corresponding assumptions and assign them to a very flexible Bayesian model. This allows us to include for instance missing observations and use prior parameter knowledge. Technically, we propose a Gaussian graphical model with latent variables to include all structural information from the data. The parameters and latent variables are estimated using expectation maximization, where we exploit the restrictions ...
Abstract. The inference and modeling of network-like structures in genomic data is of prime im-porta...
Global genetic networks provide additional information for the analysis of human diseases, beyond th...
Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene r...
Illner K, Fuchs C, Theis FJ. Blind source separation using latent Gaussian graphical models. In: Lar...
Illner K, Fuchs C, Theis FJ. Bayesian Blind Source Separation for Data with Network Structure. Journ...
In biology, more and more information about the interactions in regulatory systems becomes accessibl...
In the study of gene regulatory networks, more and more quantitative data becomes available. However...
Decoding complex relationships among large numbers of variables with relatively few observations is ...
International audienceOur concern is selecting the concentration matrix's nonzero coefficients for a...
In this thesis we aim to identify meaningful signals from observed multivariate mixtures using avail...
Graphical models provide a rich framework for summarizing the dependencies among variables. The grap...
The Gaussian graphical model is one of the most used tools for inferring genetic networks. Nowadays,...
Naturally, genes interact with each other by forming a complicated network and the relationship betw...
Decoding complex relationships among large numbers of variables with relatively few observations is ...
One of the most important and challenging ``knowledge extraction' tasks in bioinformatics is the rev...
Abstract. The inference and modeling of network-like structures in genomic data is of prime im-porta...
Global genetic networks provide additional information for the analysis of human diseases, beyond th...
Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene r...
Illner K, Fuchs C, Theis FJ. Blind source separation using latent Gaussian graphical models. In: Lar...
Illner K, Fuchs C, Theis FJ. Bayesian Blind Source Separation for Data with Network Structure. Journ...
In biology, more and more information about the interactions in regulatory systems becomes accessibl...
In the study of gene regulatory networks, more and more quantitative data becomes available. However...
Decoding complex relationships among large numbers of variables with relatively few observations is ...
International audienceOur concern is selecting the concentration matrix's nonzero coefficients for a...
In this thesis we aim to identify meaningful signals from observed multivariate mixtures using avail...
Graphical models provide a rich framework for summarizing the dependencies among variables. The grap...
The Gaussian graphical model is one of the most used tools for inferring genetic networks. Nowadays,...
Naturally, genes interact with each other by forming a complicated network and the relationship betw...
Decoding complex relationships among large numbers of variables with relatively few observations is ...
One of the most important and challenging ``knowledge extraction' tasks in bioinformatics is the rev...
Abstract. The inference and modeling of network-like structures in genomic data is of prime im-porta...
Global genetic networks provide additional information for the analysis of human diseases, beyond th...
Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene r...