ArticleBMC bioinformatics2012
Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Data mining and mathematical models in cancer prognosis and prediction.Medical review (2021) · 2022Review
- Quantification of the Landscape for Revealing the Underlying Mechanism of Intestinal-Type Gastric Cancer.Frontiers in oncology · 2022Article
- WASABI: a dynamic iterative framework for gene regulatory network inference.BMC bioinformatics · 2019Article
- Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.Oncotarget · 2017Article
- Inference of Gene Regulatory Networks Using Bayesian Nonparametric Regression and Topology Information.Computational and mathematical methods in medicine · 2017Article
- Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems.Advances in bioinformatics · 2017Article
- Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders.BMC medical genomics · 2016Article
- High-order dynamic Bayesian Network learning with hidden common causes for causal gene regulatory network.BMC bioinformatics · 2015Article
- Dynamic Bayesian Network Modeling of the Interplay between EGFR and Hedgehog Signaling.PloS one · 2015Article
- Inference of SNP-gene regulatory networks by integrating gene expressions and genetic perturbations.BioMed research international · 2014Article
- A model of the circadian clock in the cyanobacterium Cyanothece sp. ATCC 51142.BMC bioinformatics · 2013Article
- Chapter 5: Network biology approach to complex diseases.PLoS computational biology · 2012Article
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Authors and funding
4 authors.
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Abstract
backgroundDynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks.
resultsTo overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT+, employs the recently proposed information theoretic scoring metric named mutual information test (MIT). GlobalMIT+ is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques.
conclusionsOur studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.