A mixture copula Bayesian network model for multimodal genomic data

Zhang, Qingyang; Shi, Xuan

doi:10.1177/1176935117702389

Cited by 12 publications

(7 citation statements)

References 22 publications

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“…Already Hauser and Bühlmann (2015) mentioned that the Gaussian assumption for the Sachs data is questionable. This was also noticed by Voorman et al (2014), Zhang and Shi (2017). Voorman et al (2014) developed a nonparametric approach to include non Gaussian behavior in a graphical model, while Zhang and Shi (2017) build a Bayesian network using a mixture copula on d dimensions to model the conditional distribution of a node X i given the observed values of the parents π(X i ) = π(x i ) to accommodate the non Gaussianity of the data and the pooling over the different experimental conditions.…”

Section: Introductionmentioning

confidence: 85%

Analysis of an interventional protein experiment using a vine copula based structural equation model

Czado¹,

Scharl²

2021

Preprint

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While there is considerable effort to identify signaling pathways using linear Gaussian Bayesian networks from data, there is less emphasis of understanding and quantifying conditional densities and probabilities of nodes given its parents from the identified Bayesian network. Most graphical models for continuous data assume a multivariate Gaussian distribution, which might be too restrictive. We reanalyse data from an experimental setting considered in Sachs et al. (2005) to illustrate the effects of such restrictions. For this we propose a novel non Gaussian nonlinear structural equation model based on vine copulas. In particular the D-vine regression approach of Kraus and Czado (2017) is adapted. We show that this model class is more suited to fit the data than the standard linear structural equation model based on the biological consent graph given in Sachs et al. (2005). The modelling approach also allows to study which pathway edges are supported by the data and which can be removed. For data experiment cd3cd28 + aktinhib this approach identified three edges, which are no longer supported by the data. For each of these edges a plausible explanation based on underlying the experimental conditions could be found.

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Section: Introductionmentioning

confidence: 85%

Analysis of an interventional protein experiment using a vine copula based structural equation model

Czado¹,

Scharl²

2021

Preprint

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“…To model the correlation between genes, we use a Gaussian copula as it is convenient for multivariate problem. Another possible future work is to compare different latent variables and copula functions, for example, Student's t copula (Nelson, ) and Gaussian mixture copula (Zhang and Shi, ), in a model comparison framework.…”

Section: Discussionmentioning

confidence: 99%

Classification of RNA‐Seq data via Gaussian copulas

Zhang

2017

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RNA-sequencing (RNA-Seq) has become a preferred option to quantify gene expression, because it is more accurate and reliable than microarrays. In RNA-Seq experiments, the expression level of a gene is measured by the count of short reads that are mapped to the gene region. Although some normal-based statistical methods may also be applied to log-transformed read counts, they are not ideal for directly modelling RNA-Seq data. Two discrete distributions, Poisson distribution and negative binomial distribution, have been commonly used in the literature to model RNA-Seq data, where the latter is a natural extension of the former with allowance of overdispersion. Because of the technical difficulty in modelling correlated counts, most existing classifiers based on discrete distributions assume that genes are independent of each other. However, as we show in this paper, the independence assumption may cause non-ignorable bias in estimating the discriminant score, making the classification inaccurate. To this end, we drop the independence assumption and explicitly model the dependence between genes using a Gaussian copula. We apply a Bayesian approach to estimate the covariance matrix and the overdispersion parameter in negative binomial distribution. Both synthetic data and real data are used to demonstrate the advantages of our model.

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“…The variables are random, and uncertainty can be measured [20]. The applicability of Bayesian models [21], networks [22][23][24], or successful combinations [25], e.g., with gaussian variables [26], is too broad. Certainly, Bayesian methods are more difficult to implement than traditional methods, especially in epidemiology and infection diseases [27].…”

Section: Overview Of Mathematical Models To Predict Infectious Diseasesmentioning

confidence: 99%

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

2019

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Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.

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A mixture copula Bayesian network model for multimodal genomic data

Cited by 12 publications

References 22 publications

Analysis of an interventional protein experiment using a vine copula based structural equation model

Analysis of an interventional protein experiment using a vine copula based structural equation model

Classification of RNA‐Seq data via Gaussian copulas

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

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