GlobalMIT: learning globally optimal dynamic bayesian network with the mutual information test criterion

Vinh, Nguyen X.; Chetty, Madhu; Coppel, Ross L.; Wangikar, Pramod P.

doi:10.1093/bioinformatics/btr457

Cited by 72 publications

(66 citation statements)

References 10 publications

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“…Some network-learning packages are not Bayesian, but instead use other formalisms for defining statistical relationships between variables, such as GlobalMIT [18], which uses mutual information to learn a dynamic (non-Bayesian) network. Also in this category is the Uninet package which relies on conditional rank correlation to define statistical dependence between variables [19].…”

Section: Introductionmentioning

confidence: 99%

CGBayesNets: Conditional Gaussian Bayesian Network Learning and Inference with Mixed Discrete and Continuous Data

2014

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Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com.

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Section: Introductionmentioning

confidence: 99%

CGBayesNets: Conditional Gaussian Bayesian Network Learning and Inference with Mixed Discrete and Continuous Data

2014

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“…In structure learning of static or dynamic causal Bayesian networks, numerous works [2,19,18,4,5] have been proposed. However, these…”

Section: Related Workmentioning

confidence: 99%

Active Learning of Functional Networks from Spike Trains

Liu

2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Learning functional networks from spike trains is a fundamental problem with many critical applications in neuroscience. However, most of existing works focus on inferring the functional network purely from observational data, which could lead to undiscovered or spurious connections. We demonstrate that by adopting experimental data with interventions applied, the accuracy of the inferred network can be significantly improved. Nevertheless, doing interventions in real experiments is often expensive and must be chosen with care. Hence, in this paper, we design an active learning framework to iteratively choose interventions and learn the functional network. In particular, we propose two models, the variance model and the validation model, to effectively select the most informative interventions. The variance model works best to reveal undiscovered connections while the validation model has the advantage of eliminating spurious connections. Experimental results with both synthetic and real datasets show that when these two models are applied, we could achieve substantially better accuracy than using the same amount of observational data or other baseline methods to choose interventions.

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“…Bayesian networks (BN), on the other hand, are more sophisticated models with the strong foundation of probability and statistics, in which the dependencies between nodes are represented using directed edges and conditional probability distributions. Dynamic Bayesian network (DBN) [5] is a temporal form of BN which allows the modeling of system dynamics in discrete time.…”

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confidence: 99%

Inferring large scale genetic networks with S-system model

Chowdhury

Chetty

Vinh

2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

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Gene regulatory network (GRN) reconstruction from highthroughput microarray data is an important problem in systems biology. The S-System model, a differential equation based approach, is among the mainstream approaches for modeling GRNs. It has the ability to represent GRNs accurately with precise regulatory weights. However, the current applications of S-System are limited to small and medium scale network, as inferring large network requires inhibitive computational cost. In this paper, we propose a novel SSystem based framework to reconstruct biologically relevant GRNs by exploiting their special topological structure. In GRNs, the complex interactions occurring amongst transcription factors (TFs) and target genes (TGs) are unidirectional, i.e., TFs to TGs, and the vice-versa is biologically irrelevant. In addition, TFs can regulate themselves while only self-regulations may exist for TGs. As such, we decompose GRN into two sub-networks representing TF-TF and TF-TG interactions. We learn the sub-networks separately by adapting the traditional S-System model, and combining the solutions to get the entire network. Our experimental studies indicate that the proposed approach can scale up to larger networks, not achievable with other current S-System based approaches, yet with higher accuracy.

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GlobalMIT: learning globally optimal dynamic bayesian network with the mutual information test criterion

Abstract: Supplementary data is available at Bioinformatics online.

Cited by 72 publications

References 10 publications

CGBayesNets: Conditional Gaussian Bayesian Network Learning and Inference with Mixed Discrete and Continuous Data

CGBayesNets: Conditional Gaussian Bayesian Network Learning and Inference with Mixed Discrete and Continuous Data

Active Learning of Functional Networks from Spike Trains

Inferring large scale genetic networks with S-system model

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