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DOI . ORG {}

  1. Analyzed Page
  2. Matching Content Categories
  3. CMS
  4. Monthly Traffic Estimate
  5. How Does Doi.org Make Money
  6. Keywords
  7. Topics
  8. Questions
  9. Schema
  10. External Links
  11. Analytics And Tracking
  12. Libraries
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We began analyzing https://link.springer.com/protocol/10.1007/978-1-4939-3743-1_8, but it redirected us to https://link.springer.com/protocol/10.1007/978-1-4939-3743-1_8. The analysis below is for the second page.

Title[redir]:
Evaluating Computational Gene Ontology Annotations | SpringerLink
Description:
Two avenues to understanding gene function are complementary and often overlapping: experimental work and computational prediction. While experimental annotation generally produces high-quality annotations, it is low throughput. Conversely, computational annotations...

Matching Content Categories {📚}

  • Education
  • Science
  • Careers

Content Management System {📝}

What CMS is doi.org built with?

Custom-built

No common CMS systems were detected on Doi.org, and no known web development framework was identified.

Traffic Estimate {📈}

What is the average monthly size of doi.org audience?

🏙️ Massive Traffic: 50M - 100M visitors per month


Based on our best estimate, this website will receive around 80,904,851 visitors per month in the current month.

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How Does Doi.org Make Money? {💸}

We can't figure out the monetization strategy.

Websites don't always need to be profitable; some serve as platforms for education or personal expression. Websites can serve multiple purposes. And this might be one of them. Doi.org has a secret sauce for making money, but we can't detect it yet.

Keywords {🔍}

annotations, computational, function, annotation, pubmed, gene, google, scholar, experimental, protein, methods, article, ontology, prediction, proteins, predictions, information, database, central, evaluation, number, cas, škunca, open, evidence, dessimoz, chapter, data, quality, biological, terms, evaluating, biology, databases, genes, biol, access, sequence, standard, handbook, molecular, evaluate, knowledge, set, combrex, bioinformatics, validation, functional, based, gold,

Topics {✒️}

mapping swiss-prot keywords c-type lectin mincle open access chapter general enzymatic screens open access charges existing high-quality annotations preclude high-quality predictions high-quality experimental annotations produce high-probability annotations open access license gene ontology handbook identifying membrane-bound proteins privacy choices/manage cookies activity-based proteomic approaches labor-intensive nature protein/protein interaction data thomas pd gene ontology annotations quick-fix solution activity-based protein profiling gene ontology consortium open world assumption function propagates annotations reference genome project existing experimental annotations full access high throughput screening open/closed world gene function status oma orthology database full function space predicted ontological annotations gold standard dataset gold standard dataset existing experimental information european economic area performing extensive experimentation controlled vocabulary established practical obstacles interfere major practical obstacle sonic hedgehog entry key maximally informative targets dramatic cost reductions cravatt bf text-mining systems street gower st assessing computational prediction specific gene families function prediction models

Questions {❓}

  • Škunca N, Dessimoz C (2015) Phylogenetic profiling: how much input data is enough?

Schema {🗺️}

ScholarlyArticle:
      headline:Evaluating Computational Gene Ontology Annotations
      pageEnd:109
      pageStart:97
      image:https://media.springernature.com/w153/springer-static/cover/book/978-1-4939-3743-1.jpg
      genre:
         Springer Protocols
      isPartOf:
         name:The Gene Ontology Handbook
         isbn:
            978-1-4939-3743-1
            978-1-4939-3741-7
         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:Nives Škunca
            affiliation:
                  name:ETH Zurich
                  address:
                     name:Department of Computer Science, ETH Zurich, Zurich, Switzerland
                     type:PostalAddress
                  type:Organization
                  name:SIB Swiss Institute of Bioinformatics
                  address:
                     name:SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
                     type:PostalAddress
                  type:Organization
                  name:University College London
                  address:
                     name:University College London, London, UK
                     type:PostalAddress
                  type:Organization
            email:[email protected]
            type:Person
            name:Richard J. Roberts
            affiliation:
                  name:New England Biolabs
                  address:
                     name:New England Biolabs, Ipswich, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Martin Steffen
            affiliation:
                  name:Boston University
                  address:
                     name:Department of Biomedical Engineering, Boston University, Boston, USA
                     type:PostalAddress
                  type:Organization
                  name:Boston University School of Medicine
                  address:
                     name:Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, USA
                     type:PostalAddress
                  type:Organization
            type:Person
      keywords:Gene ontology, Evaluation, Tools, Prediction, Annotation, Function
      description:Two avenues to understanding gene function are complementary and often overlapping: experimental work and computational prediction. While experimental annotation generally produces high-quality annotations, it is low throughput. Conversely, computational annotations have broad coverage, but the quality of annotations may be variable, and therefore evaluating the quality of computational annotations is a critical concern. In this chapter, we provide an overview of strategies to evaluate the quality of computational annotations. First, we discuss why evaluating quality in this setting is not trivial. We highlight the various issues that threaten to bias the evaluation of computational annotations, most of which stem from the incompleteness of biological databases. Second, we discuss solutions that address these issues, for example, targeted selection of new experimental annotations and leveraging the existing experimental annotations.
      datePublished:2017
      isAccessibleForFree:1
      context:https://schema.org
Book:
      name:The Gene Ontology Handbook
      isbn:
         978-1-4939-3743-1
         978-1-4939-3741-7
Organization:
      name:Springer New York
      logo:
         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
         type:ImageObject
      name:ETH Zurich
      address:
         name:Department of Computer Science, ETH Zurich, Zurich, Switzerland
         type:PostalAddress
      name:SIB Swiss Institute of Bioinformatics
      address:
         name:SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
         type:PostalAddress
      name:University College London
      address:
         name:University College London, London, UK
         type:PostalAddress
      name:New England Biolabs
      address:
         name:New England Biolabs, Ipswich, USA
         type:PostalAddress
      name:Boston University
      address:
         name:Department of Biomedical Engineering, Boston University, Boston, USA
         type:PostalAddress
      name:Boston University School of Medicine
      address:
         name:Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, USA
         type:PostalAddress
ImageObject:
      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      name:Nives Škunca
      affiliation:
            name:ETH Zurich
            address:
               name:Department of Computer Science, ETH Zurich, Zurich, Switzerland
               type:PostalAddress
            type:Organization
            name:SIB Swiss Institute of Bioinformatics
            address:
               name:SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
               type:PostalAddress
            type:Organization
            name:University College London
            address:
               name:University College London, London, UK
               type:PostalAddress
            type:Organization
      email:[email protected]
      name:Richard J. Roberts
      affiliation:
            name:New England Biolabs
            address:
               name:New England Biolabs, Ipswich, USA
               type:PostalAddress
            type:Organization
      name:Martin Steffen
      affiliation:
            name:Boston University
            address:
               name:Department of Biomedical Engineering, Boston University, Boston, USA
               type:PostalAddress
            type:Organization
            name:Boston University School of Medicine
            address:
               name:Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, USA
               type:PostalAddress
            type:Organization
PostalAddress:
      name:Department of Computer Science, ETH Zurich, Zurich, Switzerland
      name:SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
      name:University College London, London, UK
      name:New England Biolabs, Ipswich, USA
      name:Department of Biomedical Engineering, Boston University, Boston, USA
      name:Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, USA

External Links {🔗}(227)

Analytics and Tracking {📊}

  • Google Tag Manager

Libraries {📚}

  • Clipboard.js

Emails and Hosting {✉️}

Mail Servers:

  • mx.zoho.eu
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Name Servers:

  • josh.ns.cloudflare.com
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CDN Services {📦}

  • Pbgrd

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