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XMRF: an R package to fit Markov Networks to high-throughput genetics data

Ying-Wooi Wan, Genevera I. Allen, Yulia Baker, Eunho Yang, Pradeep Ravikumar, Matthew Anderson, Zhandong Liu
2016 BMC Systems Biology  
Encoding the models and estimation techniques of the recently proposed exponential family Markov Random Fields (Yang et al., 2012), our software can be used to learn genetic networks from RNA-sequencing  ...  Conclusions: XMRF is the only tool that allows network structure learning using the native distribution of the data instead of the standard Gaussian.  ...  Declarations The publication costs for this article were funded by the corresponding author. This article has been published as part of BMC Systems Biology Volume  ... 
doi:10.1186/s12918-016-0313-0 pmid:27586041 pmcid:PMC5009817 fatcat:fjmghbciqzf4bh2sgs5v7so3ku

Function approximation approach to the inference of reduced NGnet models of genetic networks

Shuhei Kimura, Katsuki Sonoda, Soichiro Yamane, Hideki Maeda, Koki Matsumura, Mariko Hatakeyama
2008 BMC Bioinformatics  
When we use a set of differential equations to describe genetic networks, the inference problem can be defined as a function approximation problem.  ...  There does not seem to be a perfect model for the inference of genetic networks yet.  ...  This work is partially supported by the Ministry of Education, Culture, Sports, Science and Technology of Japan under Grant-in-Aid for Young Scientists (B) No. 18710166.  ... 
doi:10.1186/1471-2105-9-23 pmid:18194576 pmcid:PMC2258286 fatcat:qhcunrcugvawxj5k2hwk3yctiy

Inference and Uncertainty Quantification of Stochastic Gene Expression via Synthetic Models [article]

Kaan Ocal, Michael U Gutmann, Guido Sanguinetti, Ramon Grima
2022 bioRxiv   pre-print
The method is based on creating a tractable coarse-graining of the model that is learned from simulations, a synthetic model, to approximate the likelihood function.  ...  We demonstrate that synthetic models can substantially outperform state-of-the-art approaches on a number of nontrivial systems and datasets, yielding an accurate and computationally viable solution to  ...  As a consequence Bayesian inference for biochemical reaction networks often relies on a variety of approximations to the likelihood function [8, 10] .  ... 
doi:10.1101/2022.01.25.477666 fatcat:l5i2in2zxzcyjhygqtbl7tpbaq

XMRF: An R package to Fit Markov Networks to High-Throughput Genetics Data [article]

Ying-Wooi Wan, Genevera I. Allen, Yulia Baker, Eunho Yang, Pradeep Ravikumar, Zhandong Liu
2015 bioRxiv   pre-print
Encoding the models and estimation techniques of the recently proposed exponential family Markov Random Fields (Yang et al., 2012), our software can be used to learn genetic networks from RNA-sequencing  ...  Tools to mine this data and discover disrupted disease networks are needed as they hold the key to understanding complicated interactions between genes, mutations and aberrations, and epi-genetic markers  ...  via Ising graphical models, and epi-genetic networks via Gaussian graphical models.  ... 
doi:10.1101/032219 fatcat:fj4zo6yiffebpmuahqdyei53ia

Machine learning in bioinformatics

Pedro Larrañaga, Borja Calvo, Roberto Santana, Concha Bielza, Josu Galdiano, Iñaki Inza, José A. Lozano, Rubén Armañanzas, Guzmán Santafé, Aritz Pérez, Victor Robles
2006 Briefings in Bioinformatics  
It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization  ...  This work was partly supported by the University of the Basque Country, the Basque Government and the Ministry of Education and Science under grants 9/UPV 00140. 226  ...  Acknowledgements The authors are grateful to the anonymous reviewers for their comments, which have helped us to greatly improve this article.  ... 
doi:10.1093/bib/bbk007 pmid:16761367 fatcat:4oss26occvhkjnetcr3sesnkcu

Forecasting Rainfall Time Series with stochastic output approximated by neural networks Bayesian approach

Cristian Rodriguez, Julian Antonio
2014 International Journal of Advanced Computer Science and Applications  
We propose to use an algorithm based on artificial neural networks (ANNs) using the Bayesian inference. The result of the prediction consists of 20% of the provided data consisting of 2000 to 2010.  ...  In this sense, the time series prediction is mathematical and computational modelling series provided by monthly cumulative rainfall, which has stochastic output approximated by neural networks Bayesian  ...  In the first case, an ANNs algorithm based on Bayesian inference to model neural networks parameters were detailed.  ... 
doi:10.14569/ijacsa.2014.050623 fatcat:o7gdu6owgnfyjdjeqktutiwblm

Experimental model construction and validation of the ErbB signaling pathway

K. Kalantzaki, L. Koumakis, E. S. Bei, M. Zervakis, G. Potamias, D. Kafetzopoulos
2013 13th IEEE International Conference on BioInformatics and BioEngineering  
With the proposed framework we model the genetic interactions in the ErbB signaling pathway directly from expression data as Gaussian approximations and compare them with the KEGG canonical ErbB pathway  ...  The importance of ErbB receptor signaling in breast cancer is consistent with its functional role in normal development of mammary gland.  ...  A variety of computational methods have been considered for modeling genetic regulations in pathways, such as linear models [2] and Gaussian networks [3] that aim to provide suitable mathematical models  ... 
doi:10.1109/bibe.2013.6701557 dblp:conf/bibe/KalantzakiKBZPK13 fatcat:zfsgg5bptrgghay66mvvzsxs5u

A Log-Linear Graphical Model for Inferring Genetic Networks from High-Throughput Sequencing Data [article]

Genevera I. Allen, Zhandong Liu
2012 arXiv   pre-print
Gaussian graphical models are often used to infer gene networks based on microarray expression data.  ...  As the resulting high-dimensional count data consists of counts of sequencing reads for each gene, Gaussian graphical models are not optimal for modeling gene networks based on this discrete data.  ...  Acknowledgments The authors thank Pradeep Ravikumar for thoughtful insights and helpful discussion re-  ... 
arXiv:1204.3941v2 fatcat:apxu24mtyre7zdq5u6gfrc6bti

Modeling of Gene Regulatory Networks Using State Space Models

Samarendra Das
2017 Current Trends in Biomedical Engineering & Biosciences  
State space models are a relatively new approach to infer gene regulatory networks.  ...  Performance evaluation criteria for the approaches used for modeling genetic regulatory networks are also discussed.  ...  Non-linear state space models It may be useful to represent genetic networks by simple state-space models, to ease out computational complexity, but the main drawback of the approach is that it almost  ... 
doi:10.19080/ctbeb.2017.04.555646 fatcat:5otwe5sqbvgcxg5rmihd6juyma

Assessing Different Bayesian Neural Network Models for Militarized Interstate Dispute

Monica Lagazio, Tshilidzi Marwala
2006 Social science computer review  
This article develops and compares two Bayesian neural network models, a more restrictive Bayesian framework using Gaussian approximation and a less restrictive one using a hybrid version of Markov Chain  ...  The results indicate that the Gaussian approximation and HMC models are not statistically different in their performance.  ...  We do not rely only on the evidence to select the model for the Gaussian approximation, but instead we utilize the genetic algorithm to assess evidence results.  ... 
doi:10.1177/0894439305281512 fatcat:6ec4um6t6fabne4sr7wahpgvjq

Cancer Genetic Network Inference Using Gaussian Graphical Models

Haitao Zhao, Zhong-Hui Duan
2019 Bioinformatics and Biology Insights  
Gaussian graphical model (GGM) is often used to learn genetic networks because it defines an undirected graphical structure, revealing the conditional dependences of genes.  ...  The inferred genetic networks were examined to further identify and characterize a collection of gene interactions that are unique to cancer.  ...  Acknowledgements The authors thank the reviewers of the manuscript for their insightful thoughts and comments which have strengthened their report.  ... 
doi:10.1177/1177932219839402 pmid:31007526 pmcid:PMC6456846 fatcat:xqp72am3mvdkjmivqk4l7n3tqe

Learning Large-Scale Graphical Gaussian Models from Genomic Data

Juliane Schäfer
2005 AIP Conference Proceedings  
Here we review several recently developed approaches to small-sample inference of graphical Gaussian modeling and discuss strategies to cope with the high dimensionality of functional genomics data.  ...  The inference and modeling of network-like structures in genomic data is of prime importance in systems biology.  ...  APPENDIX: SOFTWARE The empirical Bayes approach to infer GGMs is implemented in the R package "GeneTS" (versions 2.0 and later).  ... 
doi:10.1063/1.1985393 fatcat:tulyy2rif5e6lphobcfj27uehy

Biological interaction networks based on sparse temporal expansion of graphical models

K. D. Kalantzaki, E. S. Bei, M. Garofalakis, M. Zervakis
2012 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)  
However, the small size of samples compared to the number of observed genes/proteins makes the inference of the network structure quite challenging.  ...  Many graphical models have been introduced in order to discover associations from the expression data analysis.  ...  of Gaussians basis functions [11] .  ... 
doi:10.1109/bibe.2012.6399721 dblp:conf/bibe/KalantzakiBGZ12 fatcat:2waz4zkhivegbkuyrvwqej3zpe

Reverse engineering gene regulatory networks

Yufei Huang, I. Tienda-Luna, Yufeng Wang
2009 IEEE Signal Processing Magazine  
In addition, network inference algorithms are also surveyed under the graphical modeling framework by the categories of point solutions and probabilistic solutions and the connections and differences among  ...  To provide readers with a system-level view of the modeling issues in this research, a graphical modeling framework is proposed.  ...  The project described is also supported by grant number 1SC1GM081068 from the National Institute of General Medical Sciences to Y. Wang.  ... 
doi:10.1109/msp.2008.930647 pmid:20046885 pmcid:PMC2763329 fatcat:wkewpwni4fdj5ituzfuvfwwm5i

Detecting Epistatic Selection with Partially Observed Genotype Data Using Copula Graphical Models [article]

P. Behrouzi, E.C. Wit
2017 arXiv   pre-print
The network estimation relies on penalized Gaussian copula graphical models, which accounts for a large number of markers p and a small number of individuals n.  ...  A multi-core implementation of our algorithm makes it feasible to estimate the graph in high-dimensions also in the presence of significant portions of missing data.  ...  Acknowledgements The authors would like to thank Danny Arends for his helpful suggestions with respect to the software implementation of the method.  ... 
arXiv:1710.00894v2 fatcat:k5mpf2ohpjbu7gmttui7ac5774
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