We are analyzing https://link.springer.com/article/10.1186/1471-2105-8-s2-s2.

Title:
Bayesian model-based inference of transcription factor activity | BMC Bioinformatics
Description:
Background In many approaches to the inference and modeling of regulatory interactions using microarray data, the expression of the gene coding for the transcription factor is considered to be an accurate surrogate for the true activity of the protein it produces. There are many instances where this is inaccurate due to post-translational modifications of the transcription factor protein. Inference of the activity of the transcription factor from the expression of its targets has predominantly involved linear models that do not reflect the nonlinear nature of transcription. We extend a recent approach to inferring the transcription factor activity based on nonlinear Michaelis-Menten kinetics of transcription from maximum likelihood to fully Bayesian inference and give an example of how the model can be further developed. Results We present results on synthetic and real microarray data. Additionally, we illustrate how gene and replicate specific delays can be incorporated into the model. Conclusion We demonstrate that full Bayesian inference is appropriate in this application and has several benefits over the maximum likelihood approach, especially when the volume of data is limited. We also show the benefits of using a non-linear model over a linear model, particularly in the case of repression.
Website Age:
28 years and 1 months (reg. 1997-05-29).

Matching Content Categories {📚}

Education
Photography
Science

Content Management System {📝}

What CMS is link.springer.com built with?

Custom-built

No common CMS systems were detected on Link.springer.com, and no known web development framework was identified.

Traffic Estimate {📈}

What is the average monthly size of link.springer.com audience?

🌠 Phenomenal Traffic: 5M - 10M visitors per month

Based on our best estimate, this website will receive around 7,642,828 visitors per month in the current month.

check SE Ranking
check Ahrefs
check Similarweb
check Ubersuggest
check Semrush

How Does Link.springer.com Make Money? {💸}

We're unsure how the site profits.

While profit motivates many websites, others exist to inspire, entertain, or provide valuable resources. Websites have a variety of goals. And this might be one of them. Link.springer.com has a secret sauce for making money, but we can't detect it yet.

Keywords {🔍}

model, data, gene, expression, genes, transcription, tfa, linear, inference, figure, article, bayesian, parameters, profile, pubmed, google, scholar, likelihood, delays, inferred, time, posterior, replicates, true, shown, values, factor, models, replicate, noise, parameter, analysis, full, regulatory, nonlinear, approach, microarray, due, prior, cas, information, yeast, shows, bioinformatics, fission, observed, distribution, targets, sep, target,

Topics {✒️}

𝜇 𝑔 𝑖 network component analysis epsrc grant ep/co10620/1 fission yeast cell-cycle markov-chain monte-carlo article download pdf single-cell proteomic analysis article number s2 sos-deficient escherichia coli cell-cycle synchronization methods uk/~microarray/book/smida full size image bayesian model-based inference assuming a-priori independence cell-cycle regulated motif expensive sampling-based inference gene-specific noise parameters desirable a-priori preference cell-cycle microarray data log-normal noise model privacy choices/manage cookies nonlinear michaelis-menten kinetics bmc bioinformatics 8 analysis open small cells early 𝜇 ˙ � full bayesian inference bayesian data analysis building mathematical models fully bayesian inference full access dynamic bayesian networks cell-cycle control called reverse engineering bayesian regression approach single-cell resolution single input motif biological delays specific biomed central poor parameter inference transcription factor activity conditional correlation analysis article rogers replicate specific delays conjugate gradient scheme fully bayesian framework high-throughput technology fully bayesian approach maximum likelihood scheme gene specific delays

Questions {❓}

For example, is it sufficient to use replicates as they are provided or do they need some kind of alignment beforehand?
Is the non-linear model necessary?
Com/1471-2105/8?

Schema {🗺️}

WebPage:
      mainEntity:
         headline:Bayesian model-based inference of transcription factor activity
         description:In many approaches to the inference and modeling of regulatory interactions using microarray data, the expression of the gene coding for the transcription factor is considered to be an accurate surrogate for the true activity of the protein it produces. There are many instances where this is inaccurate due to post-translational modifications of the transcription factor protein. Inference of the activity of the transcription factor from the expression of its targets has predominantly involved linear models that do not reflect the nonlinear nature of transcription. We extend a recent approach to inferring the transcription factor activity based on nonlinear Michaelis-Menten kinetics of transcription from maximum likelihood to fully Bayesian inference and give an example of how the model can be further developed. We present results on synthetic and real microarray data. Additionally, we illustrate how gene and replicate specific delays can be incorporated into the model. We demonstrate that full Bayesian inference is appropriate in this application and has several benefits over the maximum likelihood approach, especially when the volume of data is limited. We also show the benefits of using a non-linear model over a linear model, particularly in the case of repression.
         datePublished:2007-05-03T00:00:00Z
         dateModified:2007-05-03T00:00:00Z
         pageStart:1
         pageEnd:11
         license:http://creativecommons.org/licenses/by/2.0
         sameAs:https://doi.org/10.1186/1471-2105-8-S2-S2
         keywords:
            Fission Yeast
            Marginal Likelihood
            Noise Parameter
            Network Component Analysis
            Specific Delay
            Bioinformatics
            Microarrays
            Computational Biology/Bioinformatics
            Computer Appl. in Life Sciences
            Algorithms
         image:
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-8-S2-S2/MediaObjects/12859_2007_Article_1886_Fig1_HTML.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-8-S2-S2/MediaObjects/12859_2007_Article_1886_Fig2_HTML.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-8-S2-S2/MediaObjects/12859_2007_Article_1886_Fig3_HTML.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-8-S2-S2/MediaObjects/12859_2007_Article_1886_Fig4_HTML.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-8-S2-S2/MediaObjects/12859_2007_Article_1886_Fig5_HTML.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-8-S2-S2/MediaObjects/12859_2007_Article_1886_Fig6_HTML.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-8-S2-S2/MediaObjects/12859_2007_Article_1886_Fig7_HTML.jpg
         isPartOf:
            name:BMC Bioinformatics
            issn:
               1471-2105
            volumeNumber:8
            type:
               Periodical
               PublicationVolume
         publisher:
            name:BioMed Central
            logo:
               url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
               type:ImageObject
            type:Organization
         author:
               name:Simon Rogers
               affiliation:
                     name:University of Glasgow
                     address:
                        name:Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, Glasgow, UK
                        type:PostalAddress
                     type:Organization
               email:[email protected]
               type:Person
               name:Raya Khanin
               affiliation:
                     name:University of Glasgow
                     address:
                        name:Department of Statistics, University of Glasgow, Glasgow, UK
                        type:PostalAddress
                     type:Organization
               type:Person
               name:Mark Girolami
               affiliation:
                     name:University of Glasgow
                     address:
                        name:Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, Glasgow, UK
                        type:PostalAddress
                     type:Organization
               type:Person
         isAccessibleForFree:1
         type:ScholarlyArticle
      context:https://schema.org
ScholarlyArticle:
      headline:Bayesian model-based inference of transcription factor activity
      description:In many approaches to the inference and modeling of regulatory interactions using microarray data, the expression of the gene coding for the transcription factor is considered to be an accurate surrogate for the true activity of the protein it produces. There are many instances where this is inaccurate due to post-translational modifications of the transcription factor protein. Inference of the activity of the transcription factor from the expression of its targets has predominantly involved linear models that do not reflect the nonlinear nature of transcription. We extend a recent approach to inferring the transcription factor activity based on nonlinear Michaelis-Menten kinetics of transcription from maximum likelihood to fully Bayesian inference and give an example of how the model can be further developed. We present results on synthetic and real microarray data. Additionally, we illustrate how gene and replicate specific delays can be incorporated into the model. We demonstrate that full Bayesian inference is appropriate in this application and has several benefits over the maximum likelihood approach, especially when the volume of data is limited. We also show the benefits of using a non-linear model over a linear model, particularly in the case of repression.
      datePublished:2007-05-03T00:00:00Z
      dateModified:2007-05-03T00:00:00Z
      pageStart:1
      pageEnd:11
      license:http://creativecommons.org/licenses/by/2.0
      sameAs:https://doi.org/10.1186/1471-2105-8-S2-S2
      keywords:
         Fission Yeast
         Marginal Likelihood
         Noise Parameter
         Network Component Analysis
         Specific Delay
         Bioinformatics
         Microarrays
         Computational Biology/Bioinformatics
         Computer Appl. in Life Sciences
         Algorithms
      image:
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-8-S2-S2/MediaObjects/12859_2007_Article_1886_Fig1_HTML.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-8-S2-S2/MediaObjects/12859_2007_Article_1886_Fig2_HTML.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-8-S2-S2/MediaObjects/12859_2007_Article_1886_Fig3_HTML.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-8-S2-S2/MediaObjects/12859_2007_Article_1886_Fig4_HTML.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-8-S2-S2/MediaObjects/12859_2007_Article_1886_Fig5_HTML.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-8-S2-S2/MediaObjects/12859_2007_Article_1886_Fig6_HTML.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2105-8-S2-S2/MediaObjects/12859_2007_Article_1886_Fig7_HTML.jpg
      isPartOf:
         name:BMC Bioinformatics
         issn:
            1471-2105
         volumeNumber:8
         type:
            Periodical
            PublicationVolume
      publisher:
         name:BioMed Central
         logo:
            url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
            type:ImageObject
         type:Organization
      author:
            name:Simon Rogers
            affiliation:
                  name:University of Glasgow
                  address:
                     name:Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, Glasgow, UK
                     type:PostalAddress
                  type:Organization
            email:[email protected]
            type:Person
            name:Raya Khanin
            affiliation:
                  name:University of Glasgow
                  address:
                     name:Department of Statistics, University of Glasgow, Glasgow, UK
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Mark Girolami
            affiliation:
                  name:University of Glasgow
                  address:
                     name:Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, Glasgow, UK
                     type:PostalAddress
                  type:Organization
            type:Person
      isAccessibleForFree:1
["Periodical","PublicationVolume"]:
      name:BMC Bioinformatics
      issn:
         1471-2105
      volumeNumber:8
Organization:
      name:BioMed Central
      logo:
         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
         type:ImageObject
      name:University of Glasgow
      address:
         name:Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, Glasgow, UK
         type:PostalAddress
      name:University of Glasgow
      address:
         name:Department of Statistics, University of Glasgow, Glasgow, UK
         type:PostalAddress
      name:University of Glasgow
      address:
         name:Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, Glasgow, UK
         type:PostalAddress
ImageObject:
      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      name:Simon Rogers
      affiliation:
            name:University of Glasgow
            address:
               name:Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, Glasgow, UK
               type:PostalAddress
            type:Organization
      email:[email protected]
      name:Raya Khanin
      affiliation:
            name:University of Glasgow
            address:
               name:Department of Statistics, University of Glasgow, Glasgow, UK
               type:PostalAddress
            type:Organization
      name:Mark Girolami
      affiliation:
            name:University of Glasgow
            address:
               name:Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, Glasgow, UK
               type:PostalAddress
            type:Organization
PostalAddress:
      name:Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, Glasgow, UK
      name:Department of Statistics, University of Glasgow, Glasgow, UK
      name:Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, Glasgow, UK

External Links {🔗}(87)

Analytics and Tracking {📊}

Google Tag Manager

Libraries {📚}

Clipboard.js
Prism.js

CDN Services {📦}

Crossref

5.29s.

LINK . SPRINGER . COM {}