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Reading Dependencies from PolytreeLike Bayesian Networks Revisited
"... We present a graphical criterion for reading dependencies from the minimal directed independence map G of a graphoid p, under the assumption that G is a polytree and p satisfies weak transitivity. We prove that the criterion is sound and complete. We argue that assuming weak transitivity is not too ..."
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We present a graphical criterion for reading dependencies from the minimal directed independence map G of a graphoid p, under the assumption that G is a polytree and p satisfies weak transitivity. We prove that the criterion is sound and complete. We argue that assuming weak transitivity is not too restrictive. 1
BOOTSTRAP INFERENCE FOR NETWORK CONSTRUCTION WITH AN APPLICATION TO A BREAST CANCER MICROARRAY STUDY 1
"... Gaussian Graphical Models (GGMs) have been used to construct genetic regulatory networks where regularization techniques are widely used since the network inference usually falls into a high–dimension–low–sample–size scenario. Yet, finding the right amount of regularization can be challenging, espec ..."
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Gaussian Graphical Models (GGMs) have been used to construct genetic regulatory networks where regularization techniques are widely used since the network inference usually falls into a high–dimension–low–sample–size scenario. Yet, finding the right amount of regularization can be challenging, especially in an unsupervised setting where traditional methods such as BIC or crossvalidation often do not work well. In this paper, we propose a new method—Bootstrap Inference for Network COnstruction (BINCO)—to infer networks by directly controlling the false discovery rates (FDRs) of the selected edges. This method fits a mixture model for the distribution of edge selection frequencies to estimate the FDRs, where the selection frequencies are calculated via model aggregation. This method is applicable to a wide range of applications beyond network construction. When we applied our proposed method to building a gene regulatory network with microarray expression breast cancer data, we were able to identify highconfidence edges and wellconnected hub genes that could potentially play important roles in understanding the underlying biological processes of breast cancer. 1. Introduction. The
Por SaeSeaw Final Paper – Bioc218
, 2010
"... A review of graphical models for gene regulatory network inference using microarray data ..."
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A review of graphical models for gene regulatory network inference using microarray data
An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity
"... We present a sound and complete graphical criterion for reading dependencies from the minimal undirected independence map G of a graphoid M that satisfies weak transitivity. Here, complete means that it is able to read all the dependencies in M that can be derived by applying the graphoid properties ..."
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We present a sound and complete graphical criterion for reading dependencies from the minimal undirected independence map G of a graphoid M that satisfies weak transitivity. Here, complete means that it is able to read all the dependencies in M that can be derived by applying the graphoid properties and weak transitivity to the dependencies used in the construction of G and the independencies obtained from G by vertex separation. We argue that assuming weak transitivity is not too restrictive. As an intermediate step in the derivation of the graphical criterion, we prove that for any undirected graph G there exists a strictly positive discrete probability distribution with the prescribed sample spaces that is faithful to G. We also report an algorithm that implements the graphical criterion and whose running time is considered to be at most O(n 2 (e+n)) for n nodes and e edges. Finally, we illustrate how the graphical criterion can be used within bioinformatics to identify biologically meaningful gene dependencies.
Graphical Model Selection with Applications to Biological Networks
, 2009
"... Multiple Testing Procedures for ..."
RESEARCH ARTICLE
"... Gaussian graphical modeling reconstructs pathway reactions from highthroughput metabolomics data ..."
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Gaussian graphical modeling reconstructs pathway reactions from highthroughput metabolomics data
HIGHLIGHT www.rsc.org/molecularbiosystems  Molecular BioSystems 1
"... Gene networks in Arabidopsis thaliana for metabolic and environmental functions ..."
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Gene networks in Arabidopsis thaliana for metabolic and environmental functions
Submitted to the Annals of Applied Statistics BOOTSTRAP INFERENCE FOR NETWORK CONSTRUCTION WITH AN APPLICATION TO A BREAST CANCER MICROARRAY STUDY
"... Gaussian Graphical Models (GGMs) have been used to construct genetic regulatory networks where regularization techniques are widely used since the network inference usually falls into a high–dimension– low–sample–size scenario. Yet, finding the right amount of regularization can be challenging, espe ..."
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Gaussian Graphical Models (GGMs) have been used to construct genetic regulatory networks where regularization techniques are widely used since the network inference usually falls into a high–dimension– low–sample–size scenario. Yet, finding the right amount of regularization can be challenging, especially in an unsupervised setting where traditional methods such as BIC or crossvalidation often do not work well. In this paper, we propose a new method — Bootstrap Inference for Network COnstruction (BINCO) — to infer networks by directly controlling the false discovery rates (FDRs) of the selected edges. This method fits a mixture model for the distribution of edge selection frequencies to estimate the FDRs, where the selection frequencies are calculated via model aggregation. This method is applicable to a wide range of applications beyond network construction. When we applied our proposed method to building a gene regulatory network with microarray expression breast cancer data, we were able to identify