A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Filters
Learning transcriptional networks from the integration of ChIP–chip and expression data in a non-parametric model
2010
Computer applications in the biosciences : CABIOS
Preserved Fulltext
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for helpful discussions and also thank Guang Chen, Shane Jensen and Christian Stoeckert for kindly sharing the unpublished data.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
2008
Computer applications in the biosciences : CABIOS
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2011
Interface Focus
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
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
2016
Trends in Genetics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2020
Scientific Reports
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2019
Ingénierie des Systèmes d'Information
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2021
Computational and Structural Biotechnology Journal
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2017
Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2017
Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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]
2014
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2019
Information Fusion
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2018 pre-print arXiv:1807.00123v1 fatcat:zrpz2xp7ojgyjdq62xm23zvlmqOther Versions
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
2015
Nucleic Acids Research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
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
2013
Current Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
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
2004
Nucleic Acids Research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
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
2015
EURASIP Journal on Bioinformatics and Systems Biology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
« Previous
Showing results 1 — 15 out of 1,991 results