33 Hits in 8.3 sec

Uncovering Gene Regulatory Networks from Time-Series Microarray Data with Variational Bayesian Structural Expectation Maximization

Isabel Tienda Luna, Yufei Huang, Yufang Yin, Diego P. Ruiz Padillo, M. Carmen Carrion Perez
2007 EURASIP Journal on Bioinformatics and Systems Biology  
We investigate in this paper reverse engineering of gene regulatory networks from time-series microarray data. We apply dynamic Bayesian networks (DBNs) for modeling cell cycle regulations.  ...  In particular, we propose a variational Bayesian structural expectation maximization algorithm that can learn the posterior distribution of the network model parameters and topology jointly.  ...  (C.2) Figure 1 : 1 A dynamic Bayesian network modeling of time-series expression data. Figure 2 : 2 Precision-recall curve.  ... 
doi:10.1155/2007/71312 pmid:18309364 pmcid:PMC3171349 fatcat:zcbth2n2qfekjo3drl5jkniavm

Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks

Shijia Zhu, Yadong Wang
2015 Scientific Reports  
Dynamic Bayesian Networks (DBN) have been widely used to recover gene regulatory relationships from time-series data in computational systems biology.  ...  Next, we propose an improved Structural Expectation Maximization (SEM) algorithm to learn a HMDBN model from a time-series dataset.  ...  Acknowledgements We would like to thank very much the help from Eric P. Xing, Alexander J. Hartemink, and Marco Grzegorczyk.  ... 
doi:10.1038/srep17841 pmid:26680653 pmcid:PMC4683538 fatcat:a4c2qrifxbe73ocd6m3zc43oeq

Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

Harri Lähdesmäki, Sampsa Hautaniemi, Ilya Shmulevich, Olli Yli-Harja
2006 Signal Processing  
Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems  ...  A significant amount of attention has recently been focused on modeling of gene regulatory networks.  ...  Dynamic Bayesian networks (DBNs), also called dynamic probabilistic networks, are a general model class that is capable of representing complex temporal stochastic processes [16] [17] [18] .  ... 
doi:10.1016/j.sigpro.2005.06.008 pmid:17415411 pmcid:PMC1847796 fatcat:r2tcldd4rnbddimmfentvpfirm

High-order dynamic Bayesian Network learning with hidden common causes for causal gene regulatory network

Leung-Yau Lo, Man-Leung Wong, Kin-Hong Lee, Kwong-Sak Leung
2015 BMC Bioinformatics  
Due to the presence of time delays in the regulatory relationships, High-Order Dynamic Bayesian Network (HO-DBN) is a good model of GRN.  ...  Results: We have developed a discrete HO-DBN learning algorithm that can infer also hidden common cause(s) from discrete time series expression data, with some assumptions on the conditional distribution  ...  This research is partially supported by GRF Grant (Project References 414413) and GRF grant (LU310111) from the Research Grant Council of the Hong Kong Special Administrative Region.  ... 
doi:10.1186/s12859-015-0823-6 pmid:26608050 pmcid:PMC4659244 fatcat:ufdrgphdg5gshmhuz6dlyb33mi

Dynamic Bayesian Network Learning to Infer Sparse Models from Time Series Gene Expression Data

Hamda bint E Ajmal, Michael G. Madden
2021 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
Bayesian networks (BNs) and dynamic Bayesian networks (DBNs) have been widely applied to infer GRNs from gene expression data.  ...  One of the key challenges in systems biology is to derive gene regulatory networks (GRNs) from complex high-dimensional sparse data.  ...  They also proposed to extend structural expectation maximization (SEM) algorithm to learn the structure of a DBN when data is incomplete.  ... 
doi:10.1109/tcbb.2021.3092879 pmid:34181549 fatcat:drz4uqff4fcltpumh3b4t7o2ei

Reconstructing gene regulatory networks from time-series microarray data

S.P. Li, J.J. Tseng, S.C. Wang
2005 Physica A: Statistical Mechanics and its Applications  
One of the problems in reconstructing GRN is how to deal with the high dimensionality and short time course gene expression data.  ...  To overcome such difficulties, a new approach based on state space model and Expectation-Maximization (EM) ii algorithms is proposed to model the dynamic system of gene regulation and infer gene regulatory  ...  This is called the Structural Expectation Maximization (SEM) algorithm. SEM was successfully used to learn the structure of discrete DBN with missing data in [68] .  ... 
doi:10.1016/j.physa.2004.11.032 fatcat:aomzan4jwrev5ahewxykccfjie

Learning the structure of gene regulatory networks from time series gene expression data

Haoni Li, Nan Wang, Ping Gong, Edward J Perkins, Chaoyang Zhang
2011 BMC Genomics  
Dynamic Bayesian Network (DBN) is an approach widely used for reconstruction of gene regulatory networks from time-series microarray data.  ...  algorithm for GRN reconstruction with synthetic time series gene expression data generated by GeneNetWeaver and real yeast benchmark experiment data.  ...  Acknowledgements This work was supported by the US Army Corps of Engineers Environmental Quality Program under contract # W912HZ-08-2-0011and the NSF EPSCoR project "Modeling and Simulation of Complex  ... 
doi:10.1186/1471-2164-12-s5-s13 pmid:22369588 pmcid:PMC3287495 fatcat:ukzxb4lskvasxfl6mtbwd4dvwe

A review of modeling techniques for genetic regulatory networks

Hanif Yaghoobi, Siyamak Haghipour, Hossein Hamzeiy, Masoud Asadi-Khiavi
2012 Journal of Medical Signals & Sensors  
Modeling these networks is also one of the important issues in genomic signal processing. After the advent of microarray technology, it is possible to model these networks using time-series data.  ...  In this paper, we provide an extensive review of methods that have been used on time-series data and represent the features, advantages and disadvantages of each.  ...  Yu Zhang et al. used DBN with Structure Expectation Maximization (SEM) for modeling of gene network from time-series gene expression data of Saccharomyces cerevisiae.  ... 
pmid:23493097 pmcid:PMC3592506 fatcat:4vtchasgpbf4veiojlug4d2uo4

Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation

Enzo Acerbi, Teresa Zelante, Vipin Narang, Fabio Stella
2014 BMC Bioinformatics  
Results: Continuous time Bayesian networks are proposed as a new approach for gene network reconstruction from time course expression data.  ...  Continuous time Bayesian networks outperformed the other methods in terms of the accuracy of regulatory interactions learnt from data for all network sizes.  ...  Acknowledgements All the Singapore Immunology Network authors are supported by the A*STAR/Singapore Immunology Network core grant and the A*STAR Research Attachment Program (ARAP) for the graduate student  ... 
doi:10.1186/s12859-014-0387-x pmid:25495206 pmcid:PMC4267461 fatcat:wdglatakbfathec5fd547bm7la

Advances to Bayesian network inference for generating causal networks from observational biological data

J. Yu, V. A. Smith, P. P. Wang, A. J. Hartemink, E. D. Jarvis
2004 Bioinformatics  
Here, we use a simulation approach to make advances in our dynamic Bayesian network (DBN) inference algorithm, especially in the context of limited quantities of biological data.  ...  When faced with limited quantities of observational data, combining our influence score with moderate data interpolation reduces a significant portion of false positive interactions in the recovered networks  ...  ACKNOWLEDGEMENTS We thank Kurt Grandis and Derek Scott for assistance in the beginning stages of this project, and Kazuhiro Wada, Tom Smulders and other members of the Jarvis laboratory for critical comments  ... 
doi:10.1093/bioinformatics/bth448 pmid:15284094 fatcat:5h5oqggowrhsjgxfdkrxthepua

Reverse engineering gene regulatory networks

Yufei Huang, I. Tienda-Luna, Yufeng Wang
2009 IEEE Signal Processing Magazine  
Statistical models for reverse engineering gene regulatory networks are surveyed in this article.  ...  Based on the framework, we review many existing models for many aspects of gene regulation; the pros and cons of each model are discussed.  ...  His current interests include data integration for gene networks discovery, context-based classification, miRNA targets and mass spectrometry data analysis.  ... 
doi:10.1109/msp.2008.930647 pmid:20046885 pmcid:PMC2763329 fatcat:wkewpwni4fdj5ituzfuvfwwm5i

Bayesian Inference of Signaling Network Topology in a Cancer Cell Line

Steven M. Hill, Yiling Lu, Jennifer Molina, Laura M. Heiser, Paul T. Spellman, Terence P. Speed, Joe W. Gray, Gordon B. Mills, Sach Mukherjee
2012 Computer applications in the biosciences : CABIOS  
Results: In this study, we use dynamic Bayesian networks to make inferences regarding network structure and thereby generate testable hypotheses.  ...  Availability: (code and data).  ...  Saha for discussions and GlaxoSmithKline Inc. for inhibitors.  ... 
doi:10.1093/bioinformatics/bts514 pmid:22923301 pmcid:PMC3476330 fatcat:xw3zrv7xjfcpporhxwovexyowi

Advances in Bayesian network modelling: Integration of modelling technologies

Bruce G. Marcot, Trent D. Penman
2019 Environmental Modelling & Software  
Advances include improving areas of Bayesian classifiers and machine-learning algorithms for model structuring and parameterization, and development of time-dynamic models.  ...  Increasingly, BN models are being integrated with: management decision networks; structural equation modeling of causal networks; Bayesian neural networks; combined discrete and continuous variables; object-oriented  ...  Acknowledgments Inspiration for this paper comes from a keynote address given by the senior author in 2017 at the Joint Conference of the Australasian Bayesian Network Modelling Society and the Society  ... 
doi:10.1016/j.envsoft.2018.09.016 fatcat:r3r75adpbva3lbqfl5fijmg7ki

Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data

Vera-Khlara S. Oh, Robert W. Li
2021 Genes  
Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes.  ...  We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.  ...  Accordingly, in the method, Structural Expectation Maximization (SEM) and improved Bayesian Information Criterion sharing information between times are employed.  ... 
doi:10.3390/genes12030352 pmid:33673721 pmcid:PMC7997275 fatcat:ggelrkzvqjbmrgaw7pyicpmyyy

Optimization of logical networks for the modeling of cancer signaling pathways

Sébastien De Landtsheer
2019 Figshare  
PhD thesis about using logical models to infer actionable knowledge about deregulated signaling pathways in cancer  ...  Acknowledgments The authors would like to acknowledge Dr Thomas Pfau for technical help with the computations and Dr Jun Pang for valuable comments on the manuscript.  ...  Dr Dagmar Kulms, Greta del Mistro and Dr Thomas Pfau for the fruitful discussions and suggestions on the modeling and analytical pipelines.  ... 
doi:10.6084/m9.figshare.8191262 fatcat:pnk3svzclbgqxgbcnjd5fokzj4
« Previous Showing results 1 — 15 out of 33 results