Learning Bayesian networks: The combination of knowledge and statistical data

Heckerman, David; Geiger, Dan; Chickering, David Maxwell

doi:10.1007/bf00994016

Cited by 1,684 publications

(882 citation statements)

References 27 publications

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“…The features of the training set comprising the known effectors and the noneffectors were used to train four classifiers: (i) naive Bayes (43,44); (ii) Bayesian networks (45), using the Tree Augmented Bayes Network (TAN) search algorithm (46) to search for network structure; (iii) support vector machine (SVM) (47,48), with a radial basis function (RBF) kernel; and (iv) random forest (49). The "Wrapper" procedure for feature selection (50) was carried out for each classifier, excluding random forest, which performs feature selection internally.…”

Section: Methodsmentioning

confidence: 99%

Identification of Novel Coxiella burnetii Icm/Dot Effectors and Genetic Analysis of Their Involvement in Modulating a Mitogen-Activated Protein Kinase Pathway

et al. 2014

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c Coxiella burnetii, the causative agent of Q fever, is a human intracellular pathogen that utilizes the Icm/Dot type IVB secretion system to translocate effector proteins into host cells. To identify novel C. burnetii effectors, we applied a machine-learning approach to predict C. burnetii effectors, and examination of 20 such proteins resulted in the identification of 13 novel effectors. To determine whether these effectors, as well as several previously identified effectors, modulate conserved eukaryotic pathways, they were expressed in Saccharomyces cerevisiae. The effects on yeast growth were examined under regular growth conditions and in the presence of caffeine, a known modulator of the yeast cell wall integrity (CWI) mitogen-activated protein (MAP) kinase pathway. In the presence of caffeine, expression of the effectors CBU0885 and CBU1676 caused an enhanced inhibition of yeast growth, and the growth inhibition of CBU0388 was suppressed. Furthermore, analysis of synthetic lethality effects and examination of the activity of the CWI MAP kinase transcription factor Rlm1 indicated that CBU0388 enhances the activation of this MAP kinase pathway in yeast, while CBU0885 and CBU1676 abolish this activation. Additionally, coexpression of CBU1676 and CBU0388 resulted in mutual suppression of their inhibition of yeast growth. These results strongly indicate that these three effectors modulate the CWI MAP kinase pathway in yeast. Moreover, both CBU1676 and CBU0885 were found to contain a conserved haloacid dehalogenase (HAD) domain, which was found to be required for their activity. Collectively, our results demonstrate that MAP kinase pathways are most likely targeted by C. burnetii Icm/Dot effectors.

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Section: Methodsmentioning

confidence: 99%

Identification of Novel Coxiella burnetii Icm/Dot Effectors and Genetic Analysis of Their Involvement in Modulating a Mitogen-Activated Protein Kinase Pathway

et al. 2014

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“…Unlike many other network inference frameworks, Bayesian networks are capable of representing combinatorial, nonlinear, and stochastic relationships, such as are often found in biological systems. Additionally, due to their probabilistic nature, Bayesian networks can handle noisy data [10]. While static Bayesian networks are limited to having no cycles, regulatory cycles like those found in biological systems can be handled using dynamic Bayesian networks (DBN).…”

Section: Network Inference Algorithmmentioning

confidence: 99%

Influence of Network Topology and Data Collection on Network Inference

Smith

Jarvis²,

Hartemink³

2002

Biocomputing 2003

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We recently developed an approach for testing the accuracy of network inference algorithms by applying them to biologically realistic simulations with known network topology. Here, we seek to determine the degree to which the network topology and data sampling regime influence the ability of our Bayesian network inference algorithm, NETWORKINFERENCE, to recover gene regulatory networks. NETWORKINFERENCE performed well at recovering feedback loops and multiple targets of a regulator with small amounts of data, but required more data to recover multiple regulators of a gene. When collecting the same number of data samples at different intervals from the system, the best recovery was produced by sampling intervals long enough such that sampling covered propagation of regulation through the network but not so long such that intervals missed internal dynamics. These results further elucidate the possibilities and limitations of network inference based on biological data.

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“…Having the probability distribution (model) that describes a system of interacting variables does not, however, mean that one can readily extract useful statistical information from the model. In fact, both the model construction and the task of extracting information from the model are computationally hard, with computation times that in the worst cases grow exponentially with the number of involved variables [52][53][54][55][56]. In the following, we address the above sub-problems in addition to the main problem of optimizing an appropriate objective functional of observations, which are made during the course of diagnosis.…”

Section: Resultsmentioning

confidence: 99%

Toward First Principle Medical Diagnostics: On the Importance of Disease-Disease and Sign-Sign Interactions

Ramezanpour

Mashaghi

2017

Front. Phys.

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A fundamental problem in medicine and biology is to assign states, e.g., healthy or diseased, to cells, organs or individuals. State assignment or making a diagnosis is often a nontrivial and challenging process and, with the advent of omics technologies, the diagnostic challenge is becoming more and more serious. The challenge lies not only in the increasing number of measured properties and dynamics of the system (e.g., cell or human body) but also in the co-evolution of multiple states and overlapping properties, and degeneracy of states. We develop, from first principles, a generic rational framework for state assignment in cell biology and medicine, and demonstrate its applicability with a few simple theoretical case studies from medical diagnostics. We show how disease-related statistical information can be used to build a comprehensive model that includes the relevant dependencies between clinical and laboratory findings (signs) and diseases. In particular, we include disease-disease and sign-sign interactions and study how one can infer the probability of a disease in a patient with given signs. We perform comparative analysis with simple benchmark models to check the performances of our models. We find that including interactions can significantly change the statistical importance of the signs and diseases. This first principles approach, as we show, facilitates the early diagnosis of disease by taking interactions into accounts, and enables the construction of consensus diagnostic flow charts. Additionally, we envision that our approach will find applications in systems biology, and in particular, in characterizing the phenome via the metabolome, the proteome, the transcriptome, and the genome.

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Learning Bayesian networks: The combination of knowledge and statistical data

Cited by 1,684 publications

References 27 publications

Identification of Novel Coxiella burnetii Icm/Dot Effectors and Genetic Analysis of Their Involvement in Modulating a Mitogen-Activated Protein Kinase Pathway

Identification of Novel Coxiella burnetii Icm/Dot Effectors and Genetic Analysis of Their Involvement in Modulating a Mitogen-Activated Protein Kinase Pathway

Influence of Network Topology and Data Collection on Network Inference

Toward First Principle Medical Diagnostics: On the Importance of Disease-Disease and Sign-Sign Interactions

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