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LINK . SPRINGER . COM {}

  1. Analyzed Page
  2. Matching Content Categories
  3. CMS
  4. Monthly Traffic Estimate
  5. How Does Link.springer.com Make Money
  6. Keywords
  7. Topics
  8. Questions
  9. Schema
  10. External Links
  11. Analytics And Tracking
  12. Libraries
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We are analyzing https://link.springer.com/protocol/10.1007/978-1-61779-361-5_9.

Title:
Using Phylogenetic Profiles to Predict Functional Relationships | SpringerLink
Description:
Phylogenetic profiling involves the comparison of phylogenetic data across gene families. It is possible to construct phylogenetic trees, or related data structures, for specific gene families using a wide variety of tools and approaches. Phylogenetic profiling...
Website Age:
28 years and 1 months (reg. 1997-05-29).

Matching Content Categories {πŸ“š}

  • Education
  • Science
  • Telecommunications

Content Management System {πŸ“}

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 {πŸ“ˆ}

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 7,643,078 visitors per month in the current month.

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How Does Link.springer.com Make Money? {πŸ’Έ}

We don't see any clear sign of profit-making.

Not all websites focus on profit; some are designed to educate, connect people, or share useful tools. People create websites for numerous reasons. And this could be one such example. Link.springer.com has a revenue plan, but it's either invisible or we haven't found it.

Keywords {πŸ”}

google, scholar, article, pubmed, phylogenetic, cas, protein, functional, gene, profiles, pellegrini, bioinformatics, protocol, function, analysis, data, molecular, families, privacy, cookies, content, information, publish, search, chapter, bmc, springer, networks, biology, profiling, genes, proteins, genome, eisenberg, res, download, author, bruxelles, usd, personal, log, journal, research, bacterial, relationships, matteo, book, methods, related, evolution,

Topics {βœ’οΈ}

month download article/chapter multi-scale network visualization bacterial molecular networks privacy choices/manage cookies phylogenetic profiling involves functional gene networks device instant download trends biochem sci perform phylogenetic profiling predict functional relationships nucleic acids res european economic area perform consecutive steps uncharacterized cellular systems ucla-doe institute editor information editors rue d'ulm 46 assigning protein functions protein link explorer discovering functional linkages parallel functional modules describe phylogenetic profiles phylogenetic-statistical analyses phylogenetic profile comparisons protein-protein interaction protein phylogenetic profiles author information authors journal finder publish conditions privacy policy mol biol evol plos comput biol phylogenetic profiling technique identifying metabolic enzymes uncharacterized cellular pathways construct phylogenetic trees protein-protein interactions related data structures accepting optional cookies gene sequence evolution select reference organisms gene ontology consortium protein coding genes main content log protocol usdΒ 49 protocol pellegrini specific gene families matteo pellegrini chapter log comparative genome analysis genome-wide analysis

Questions {❓}

  • (2007) Phylogenetic profiles for the prediction of protein-protein interactions: how to select reference organisms?

Schema {πŸ—ΊοΈ}

ScholarlyArticle:
      headline:Using Phylogenetic Profiles to Predict Functional Relationships
      pageEnd:177
      pageStart:167
      image:https://media.springernature.com/w153/springer-static/cover/book/978-1-61779-361-5.jpg
      genre:
         Springer Protocols
      isPartOf:
         name:Bacterial Molecular Networks
         isbn:
            978-1-61779-361-5
            978-1-61779-360-8
         type:Book
      publisher:
         name:Springer New York
         logo:
            url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
            type:ImageObject
         type:Organization
      author:
            name:Matteo Pellegrini
            affiliation:
                  name:University of California
                  address:
                     name:Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, USA
                     type:PostalAddress
                  type:Organization
            email:[email protected]
            type:Person
      keywords:Phylogenetic profiles, Coevolution, Functional associations, Comparative genomics, Coevolving proteins
      description:Phylogenetic profiling involves the comparison of phylogenetic data across gene families. It is possible to construct phylogenetic trees, or related data structures, for specific gene families using a wide variety of tools and approaches. Phylogenetic profiling involves the comparison of this data to determine which families have correlated or coupled evolution. The underlying assumption is that in certain cases these couplings may allow us to infer that the two families are functionally related: that is their function in the cell is coupled. Although this technique can be applied to noncoding genes, it is more commonly used to assess the function of protein coding genes. Examples of proteins that are functionally related include subunits of protein complexes, or enzymes that perform consecutive steps along biochemical pathways. We hypothesize the deletion of one of the families from a genome would then indirectly affect the function of the other. Dozens of different implementations of the phylogenetic profiling technique have been developed over the past decade. These range from the first simple approaches that describe phylogenetic profiles as binary vectors to the most complex ones that attempt to model to the coevolution of protein families on a phylogenetic tree. We discuss a set of these implementations and present the software and databases that are available to perform phylogenetic profiling.
      datePublished:2012
      isAccessibleForFree:
      hasPart:
         isAccessibleForFree:
         cssSelector:.main-content
         type:WebPageElement
      context:https://schema.org
Book:
      name:Bacterial Molecular Networks
      isbn:
         978-1-61779-361-5
         978-1-61779-360-8
Organization:
      name:Springer New York
      logo:
         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
         type:ImageObject
      name:University of California
      address:
         name:Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, USA
         type:PostalAddress
ImageObject:
      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      name:Matteo Pellegrini
      affiliation:
            name:University of California
            address:
               name:Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, USA
               type:PostalAddress
            type:Organization
      email:[email protected]
PostalAddress:
      name:Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, USA
WebPageElement:
      isAccessibleForFree:
      cssSelector:.main-content

External Links {πŸ”—}(90)

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