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Modeling gene expression regulatory networks with the sparse vector autoregressive model
2007
BMC Systems Biology
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Results: We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. ...
Here, we present the Sparse Vector Autoregressive model as a solution to these problems. ...
the sparse vector autoregressive model (SVAR). ...
doi:10.1186/1752-0509-1-39
pmid:17761000
pmcid:PMC2048982
fatcat:s3nixve24rbq3calwpt4udaqyu
Sparse time series chain graphical models for reconstructing genetic networks
2013
Biostatistics
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We propose a sparse high-dimensional time series chain graphical model for reconstructing genetic networks from gene expression data parametrized by a precision matrix and autoregressive coefficient matrix ...
We use penalized likelihood inference with a smoothly clipped absolute deviation penalty to explore the relationships among the observed time course gene expressions. ...
ACKNOWLEDGMENTS The authors are grateful to the Editor, an Associate Editor, and the anonymous referee for their very valuable comments. Conflict of Interest: None declared. ...
doi:10.1093/biostatistics/kxt005
pmid:23462022
fatcat:v62vdpwdtzdzbdnwqyfu7lu5d4
Stability of building gene regulatory networks with sparse autoregressive models
2011
BMC Bioinformatics
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This paper investigates the stability of sparse auto-regressive models of building GRN from gene expression data. ...
There is an increased interest in building gene regulatory networks (GRNs) from temporal gene expression data because of their numerous applications in life sciences. ...
Acknowledgements We thank Prof Andre Fujita for providing the reduced HeLa cell cycle dataset. This work was partly supported by a ARC 9/10 grant to J. C. ...
doi:10.1186/1471-2105-12-s13-s17
pmid:22373004
pmcid:PMC3278833
fatcat:7u3g4pmyuvesjpklw7mp26ptw4
Sign: large-scale gene network estimation environment for high performance computing
2011
Genome Informatics Series
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In these three programs, five different models are available: static and dynamic nonparametric Bayesian networks, state space models, graphical Gaussian models, and vector autoregressive models. ...
Our research group is currently developing software for estimating large-scale gene networks from gene expression data. ...
Acknowledgements The development of SiGN is supported by the ISLiM (Integrated Simulation of Living Matter) project in RIKEN Computational Science Research Program. ...
pmid:22230938
fatcat:plw5ias6wrfcpps7adfr6nb274
A state space representation of VAR models with sparse learning for dynamic gene networks
2010
Genome Informatics Series
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We propose a state space representation of vector autoregressive model and its sparse learning based on L1 regularization to achieve efficient estimation of dynamic gene networks based on time course microarray ...
By comparing the estimated network with the control network estimated using non-treated lung cells, perturbed genes by the anticancer drug could be found, whose up- and down-stream genes in the estimated ...
Methods for Estimating Dynamic Gene Networks
Existing Methods Vector autoregressive model Given gene expression profile vectors of p genes during T time points {y 1 , . . . , y T }, the first order vector ...
pmid:20238419
fatcat:qt6kwlyuivdu5dmqroxzoookmq
Stable Gene Regulatory Network Modeling From Steady-State Data
2016
Bioengineering
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In this paper, the least absolute shrinkage and selection operator-vector autoregressive (LASSO-VAR) model, originally proposed for the analysis of economic time series data in [27] , is adapted to include ...
The fact that LASSO-VAR is a vector autoregressive process implies that Granger causality can be inferred. ...
Acknowledgments: The authors wish to acknowledge Michael Zavlanos for making the source codes to his genetic network identification algorithm available. ...
doi:10.3390/bioengineering3020012
pmid:28952574
pmcid:PMC5597136
fatcat:6ybul52o2jeunagb4r2cdrf4i4
GEDI: a user-friendly toolbox for analysis of large-scale gene expression data
2007
BMC Bioinformatics
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Here we present an user-friendly toolbox which allows large-scale gene expression analysis to be carried out by biomedical researchers with limited programming skills. ...
Results: Here, we introduce an user-friendly toolbox called GEDI (Gene Expression Data Interpreter), an extensible, open-source, and freely-available tool that we believe will be useful to a wide range ...
Additional file 1 This zipped file contains the GEDI R package. Click here for file [http://www.biomedcentral.com/content/supplementary/1471-2105-8-457-S1.zip] ...
doi:10.1186/1471-2105-8-457
pmid:18021455
pmcid:PMC2194737
fatcat:zjt2a7yykjaqhbg4y2jqq55kne
Bayesian Inference for Sparse VAR(1) Models, with Application to Time Course Microarray Data
2011
Journal of Biometrics & Biostatistics
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This paper considers the problem of undertaking fully Bayesian inference for both the parameters and structure of a vector autoregressive model on the basis of time course data in the "p >> n scenario" ...
The autoregressive matrix is assumed to be sparse, but of unknown structure. ...
The authors are affiliated with the Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN) at Newcastle University, which is supported jointly by the Biotechnology and Biological Sciences ...
doi:10.4172/2155-6180.1000127
fatcat:jskw6bmarjbmhngcctyaballmm
Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process
2007
BMC Bioinformatics
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Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. ...
Moreover, the analysis of expression time series data from Arabidopsis thaliana resulted in a biologically sensible network. ...
Acknowledgements We thank Papapit Ingkasuwan for pointing us to the Arabidopsis thaliana data set, and the referees for their valuable comments. ...
doi:10.1186/1471-2105-8-s2-s3
pmid:17493252
pmcid:PMC1892072
fatcat:ydvthpjkgrhb3ebjj7cbiinwxi
Discovering Graphical Granger Causality Using the Truncating Lasso Penalty
[article]
2010
arXiv
pre-print
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to discover regulatory interactions among genes. ...
Whole-genome expression data over time provides an opportunity to determine how the expression levels of genes are affected by changes in transcription levels of other genes, and can therefore be used ...
The authors would like to thank three anonymous referees for constructive comments. The work of George Michailidis was partially supported by NIH grant 1RC1CA145444-0110. ...
arXiv:1007.0499v1
fatcat:rxsxoukzevfdldhk7c5l3gxpvm
Inferring Gene Regulatory Networks from Time-Series Expressions Using Random Forests Ensemble
[chapter]
2013
Lecture Notes in Computer Science
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The efficacy of the proposed approach is demonstrated on synthetic time-series datasets and Saccharomyces cerevisiae (Yeast) microarray gene expression data with 9-genes. ...
Reconstructing gene regulatory network (GRN) from timeseries expression data has become increasingly popular since time course data contain temporal information about gene regulation. ...
Synthetic expression data generation First-order multivariate vector autoregressive model (MVAR) [10] , [9] is used to generate synthetic time-series gene expression data. ...
doi:10.1007/978-3-642-39159-0_2
fatcat:kgtc4pvphbhgvpamuwohm7h6u4
Stochastic Dynamic Modeling of Short Gene Expression Time-Series Data
2008
IEEE Transactions on Nanobioscience
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The gene regulatory network is viewed as a stochastic dynamic model, which consists of the noisy gene measurement from microarray and the gene regulation first-order autoregressive (AR) stochastic dynamic ...
In this paper, the expectation maximization (EM) algorithm is applied for modeling the gene regulatory network from gene time-series data. ...
In the near future, we will continue to investigate some real-world gene expression data sets and apply our algorithm to reconstruct gene regulatory networks with missing data, sparse connectivity, periodicity ...
doi:10.1109/tnb.2008.2000149
pmid:18334455
fatcat:pj7hoipuwje3dlylli5loqvlny
Stochastic Dynamic Modeling of Short Gene Expression Time Series Data
[chapter]
2010
Chapman & Hall/CRC Computer Science & Data Analysis
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The gene regulatory network is viewed as a stochastic dynamic model, which consists of the noisy gene measurement from microarray and the gene regulation first-order autoregressive (AR) stochastic dynamic ...
In this paper, the expectation maximization (EM) algorithm is applied for modeling the gene regulatory network from gene time-series data. ...
In the near future, we will continue to investigate some real-world gene expression data sets and apply our algorithm to reconstruct gene regulatory networks with missing data, sparse connectivity, periodicity ...
doi:10.1201/9781420091540-c10
fatcat:amzbqtnkvjhlxflrzxnau2wyaa
Adaptive Thresholding for Reconstructing Regulatory Networks from Time-Course Gene Expression Data
2011
Statistics in Biosciences
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Discovering regulatory interactions from time course gene expression data constitutes a canonical problem in functional genomics and systems biology. ...
We establish the asymptotic properties of the proposed technique, and discuss the advantages it offers over competing methods, such as the truncating lasso. ...
AS and GM would like to acknowledge the support from the Statistical and Applied Mathematical Sciences Institute (SAMSI) as participants in the program on Analysis of Complex Networks. ...
doi:10.1007/s12561-011-9050-5
fatcat:b4wwz35dknce5mjqebbsxv4qc4
Operator-valued kernel-based vector autoregressive models for network inference
2014
Machine Learning
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In this work, we introduce a novel family of vector autoregressive models based on different operator-valued kernels to identify the dynamical system and retrieve the target network that characterizes ...
For this learning task, a number of approaches primarily based on sparse linear models or Granger causality concepts have been proposed in the literature. ...
In a gene regulatory network, a gene i is said to regulate another gene j if the expression of gene i at time t influences the expression of gene j at time t + 1. ...
doi:10.1007/s10994-014-5479-3
fatcat:lv4hhzzcsrdfbcob3loii3w5mi
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