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Learning transcriptional networks from the integration of ChIP–chip and expression data in a non-parametric model

Ahrim Youn, David J. Reiss, Werner Stuetzle
2010 Computer applications in the biosciences : CABIOS  
Results: We have developed LeTICE (Learning T ranscriptional networks from the Integration of ChIP-chip and Expression data), an algorithm for learning a transcriptional network from ChIP-chip and expression  ...  We applied LeTICE to the location and expression data from yeast cells grown in rich media to learn the transcriptional network specific to the yeast cell cycle.  ...  ACKNOWLEDGEMENTS We thank Thomas Richardson for helpful discussions and also thank Guang Chen, Shane Jensen and Christian Stoeckert for kindly sharing the unpublished data.  ... 
doi:10.1093/bioinformatics/btq289 pmid:20525821 pmcid:PMC2913654 fatcat:fvegnqwl3ravzghbtytgnlvwbu

Unraveling transcriptional regulatory programs by integrative analysis of microarray and transcription factor binding data

Huai Li, Ming Zhan
2008 Computer applications in the biosciences : CABIOS  
The model takes into account the non-linear structure in gene expression data, particularly in the TF-target gene interactions and the combinatorial nature of gene regulation by TFs.  ...  Method: Here, we present a new methodology that integrates microarray and TF binding data for unraveling transcriptional regulatory networks.  ...  ACKNOWLEDGEMENTS This study is supported by the Intramural Research Program, National Institute on Aging, NIH.  ... 
doi:10.1093/bioinformatics/btn332 pmid:18586698 pmcid:PMC2519161 fatcat:g5l7bd22jvbw3hkqjvkwdlskpm

How to infer gene networks from expression profiles, revisited

C. A. Penfold, D. L. Wild
2011 Interface Focus  
Here, we revisit this work by assessing the performance of more recent network inference algorithms, including a novel non-parametric learning approach based upon nonlinear dynamical systems.  ...  Inferring the topology of a gene-regulatory network (GRN) from genome-scale time-series measurements of transcriptional change has proved useful for disentangling complex biological processes.  ...  We acknowledge support from grants BBRSC BB/F005806/1 (Plant Response to Environmental Stress in Arabidopsis; C.A.P. and D.L.W.) and EPSRC EP/ G021163/1 (Mathematics of Complexity Science and Systems Biology  ... 
doi:10.1098/rsfs.2011.0053 pmid:23226586 pmcid:PMC3262295 fatcat:glhjailtdngwzmi3zcnb4keo5y

Past Roadblocks and New Opportunities in Transcription Factor Network Mapping

Michael R. Brent
2016 Trends in Genetics  
Binding potential Models of TF binding specificity obtained from in vitro experiments complement in vivo location methods like ChIP-seq and can provide additional information about whether a physical interaction  ...  These new data types demand new computational approaches that can effectively analyze and integrate them for network mapping.  ...  M.B. was supported in part by grant GM100452 from the National Institute of General Medical Sciences of the NIH.  ... 
doi:10.1016/j.tig.2016.08.009 pmid:27720190 pmcid:PMC5117949 fatcat:jj2k3sxne5birgn2e46psxj5eu

OutPredict: multiple datasets can improve prediction of expression and inference of causality

Jacopo Cirrone, Matthew D. Brooks, Richard Bonneau, Gloria M. Coruzzi, Dennis E. Shasha
2020 Scientific Reports  
We find that gene expression models can benefit from the addition of steady-state data to predict expression values of time series.  ...  Here we present a method called OutPredict that constructs a model for each gene based on time series (and other) data and that predicts gene's expression in a previously unseen subsequent time point.  ...  1F32GM116347 to M.D.B., and a Plant Genomics Grant from the Zegar Family Foundation (A160051).  ... 
doi:10.1038/s41598-020-63347-3 pmid:32321967 fatcat:xvj6awu3pbh6pe7s44kbkgm3d4

ANOVAG3: A Hybrid Algorithm for Inferring Gene Regulatory Network Using Time Series Gene Expression Data

Shaimaa M. Elembaby, Vidan F. Ghoneim, Manal Abdel-Wahed
2019 Ingénierie des Systèmes d'Information  
Integration between one-way ANOVA and GENIE3 is a hybrid algorithm entitled ANOVAG3. ANOVAG3 is applied only on time series gene expressions and takes less running time than GENIE3 with huge data.  ...  Although ANOVAG3 is not dependent on perturbation data or transcription factors, it records comparable results for networks 1 and 3 and records best results for network 4 (AUROC =0.5628) of DREAM5 challenge  ...  MATERIALS AND METHODS Data set DREAM5 provides data of each network in three files: chip feature, gene expression and transcription factor (TF) file.  ... 
doi:10.18280/isi.240301 fatcat:jiuhjylxgvbrrd2zt724vtxtyq

Computational biology approaches for mapping transcriptional regulatory networks

Violaine Saint-André
2021 Computational and Structural Biotechnology Journal  
Transcriptional Regulatory Networks (TRNs) are mainly responsible for the cell-type or cell-state -specific expression of gene sets from the same DNA sequence.  ...  Most recent modelling of TRNs using other types of molecular data or integrating different data types, including single-cell RNA-sequencing and chromatin information, will then be discussed, before briefly  ...  It trains random forest models to predict the expression of each gene in the data set from the expression of TFs passed in input.  ... 
doi:10.1016/j.csbj.2021.08.028 pmid:34522292 pmcid:PMC8426465 fatcat:radqgznjybfo7iey55vjd2ntwm

NetProphet 2.0: mapping transcription factor networks by exploiting scalable data resources

Yiming Kang, Hien-Haw Liow, Ezekiel J Maier, Michael R Brent, Cenk Sahinalp
2017 Bioinformatics  
Previous work has described network mapping algorithms that rely exclusively on gene expression data and 'integrative' algorithms that exploit a wide range of data sources including chromatin immunoprecipitation  ...  Third, even a noisy, preliminary network map can be used to infer DNA binding specificities from promoter sequences and these inferred specificities can be used to further improve the accuracy of the network  ...  Instead, it uses a non-linear, non-parametric regression model based on random forests to predict the effects of a TF perturbation on the expression of a gene.  ... 
doi:10.1093/bioinformatics/btx563 pmid:28968736 pmcid:PMC5860202 fatcat:3twtecqz6fhqtg5g4oscqmddnq

Efficient inference for sparse latent variable models of transcriptional regulation

Zhenwen Dai, Mudassar Iqbal, Neil D Lawrence, Magnus Rattray, Jonathan Wren
2017 Bioinformatics  
Sparse latent factor models, assuming activity of transcription factors (TFs) as unobserved, provide a biologically interpretable modelling framework, integrating gene expression and genome-wide binding  ...  We validate our method on synthetic data against a similar model in the literature, employing MCMC for inference, and obtain comparable results with a small fraction of the computational time.  ...  Data used was generated in whole or in part with Federal funds from the National Institute of Allergy and Infectious Diseases, National Institute of Health, Department of Health and Human Services, under  ... 
doi:10.1093/bioinformatics/btx508 pmid:28961802 pmcid:PMC5860323 fatcat:tsf3ui234rcmteypm3o53gkslu

Learning modular structures from network data and node variables [article]

Elham Azizi, James E. Galagan, Edoardo M. Airoldi
2014 arXiv   pre-print
We illustrate theoretical and practical significance of the model and develop a reversible-jump MCMC learning procedure for learning modules and model parameters.  ...  We demonstrate the method accuracy in predicting modular structures from synthetic data and capability to learn influence structures in twitter data and regulatory modules in the Mycobacterium tuberculosis  ...  Acknowledgments We acknowledge funding from the Hariri Institute for Computing and Computational Science & Engineering, the National Institute of Health under grants HHSN272200800059C and R01 GM096193,  ... 
arXiv:1405.2566v1 fatcat:xvpbjt26eveglmrwb6agzoineq

Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities

Marinka Zitnik, Francis Nguyen, Bo Wang, Jure Leskovec, Anna Goldenberg, Michael M. Hoffman
2019 Information Fusion  
In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine.  ...  The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view.  ...  While a powerful non-parametric framework, PSDF suffers from high computational costs due to the necessity to infer a large number of parameters and the restriction to combine only two data types.  ... 
doi:10.1016/j.inffus.2018.09.012 pmid:30467459 pmcid:PMC6242341 fatcat:mjhnzxxv4fbrlgufb7vkg3pz5u

Simultaneous characterization of sense and antisense genomic processes by the double-stranded hidden Markov model

Julia Glas, Sebastian Dümcke, Benedikt Zacher, Don Poron, Julien Gagneur, Achim Tresch
2015 Nucleic Acids Research  
We applied dsHMM to yeast using strand specific transcription data, nucleosome data, and protein binding data for a set of 11 factors associated with the regulation of transcription.The resulting annotation  ...  We present the double-stranded HMM (dsHMM), a model for the strand-specific analysis of genomic processes.  ...  ACKNOWLEDGEMENTS We thank Björn Schwalb, Patrick Cramer and Michael Lidschreiber for their help in data preprocessing, for stimulating discussions and valuable suggestions that improved the paper.  ... 
doi:10.1093/nar/gkv1184 pmid:26578558 pmcid:PMC4797261 fatcat:iwaprlieirh7tkujkerhlmrg2y

Bioinformatics Studies on Induced Pluripotent Stem Cell

Yong Wang
2013 Current Bioinformatics  
Intensive follow-up studies have accumulated a large amount of high-throughput data in transcription, proteomics, methylation, and other levels, which makes the computational studies feasible.  ...  The induced pluripotent stem cells (iPSCs), generated from transcription factor-induced reprogramming, hold the great promise as the next generation materials for regenerative medicine.  ...  This work is supported by National Natural Science Foundation of China (NSFC) under Grant 61171007 and 11131009.  ... 
doi:10.2174/1574893611308010013 fatcat:sthrsebrbjd7dda4tvbf6knfme

Identifying estrogen receptor target genes using integrated computational genomics and chromatin immunoprecipitation microarray

V. X. Jin
2004 Nucleic Acids Research  
This integrated approach, therefore, sets a paradigm in which the iterative process of model refinement and experimental verification will continue until an accurate prediction of promoter target sequences  ...  The estrogen receptor a (ERa) regulates gene expression by either direct binding to estrogen response elements or indirect tethering to other transcription factors on promoter targets.  ...  Pohar and Saranyan K.  ... 
doi:10.1093/nar/gkh1005 pmid:15608294 pmcid:PMC545447 fatcat:n6h5qhjijbaqtbvb5mgm7vfhaa

Analysis of miRNA, mRNA, and TF interactions through network-based methods

Pietro H Guzzi, Maria Teresa Di Martino, Pierosandro Tagliaferri, Pierfrancesco Tassone, Mario Cannataro
2015 EURASIP Journal on Bioinformatics and Systems Biology  
The comprehensive analysis is made possible only by the integration and the analysis of these data sources.  ...  The need for an introductive survey from a computer science point of view consequently arises. This survey starts by discussing general concepts related to production of data.  ...  Acknowledgements This work has been supported by the Italian Association for Cancer Research (AIRC), PI: PT. "Special Program Molecular Clinical Oncology -5 per mille" n.  ... 
doi:10.1186/s13637-015-0023-8 pmid:28194173 pmcid:PMC5270379 fatcat:ghnojnoitbes3gmvlbmwxqgspe
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