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The Gene Ontology Handbook

Overview of attention for book
The Gene Ontology Handbook
Humana Press

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Primer on Ontologies
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    Chapter 2 The Gene Ontology and the Meaning of Biological Function
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    Chapter 3 Primer on the Gene Ontology
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    Chapter 4 Best Practices in Manual Annotation with the Gene Ontology
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    Chapter 5 Computational Methods for Annotation Transfers from Sequence
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    Chapter 6 Text Mining to Support Gene Ontology Curation and Vice Versa
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    Chapter 7 How Does the Scientific Community Contribute to Gene Ontology?
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    Chapter 8 Evaluating Computational Gene Ontology Annotations
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    Chapter 9 Evaluating Functional Annotations of Enzymes Using the Gene Ontology
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    Chapter 10 Community-Wide Evaluation of Computational Function Prediction
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    Chapter 11 Get GO! Retrieving GO Data Using AmiGO, QuickGO, API, Files, and Tools
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    Chapter 12 Semantic Similarity in the Gene Ontology
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    Chapter 13 Gene-Category Analysis
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    Chapter 14 Gene Ontology: Pitfalls, Biases, and Remedies
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    Chapter 15 Visualizing GO Annotations
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    Chapter 16 A Gene Ontology Tutorial in Python
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    Chapter 17 Annotation Extensions
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    Chapter 18 The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations
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    Chapter 19 Complementary Sources of Protein Functional Information: The Far Side of GO
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    Chapter 20 Integrating Bio-ontologies and Controlled Clinical Terminologies: From Base Pairs to Bedside Phenotypes
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    Chapter 21 The Vision and Challenges of the Gene Ontology
Attention for Chapter 18: The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations
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Chapter title
The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations
Chapter number 18
Book title
The Gene Ontology Handbook
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-3743-1_18
Pubmed ID
Book ISBNs
978-1-4939-3741-7, 978-1-4939-3743-1
Authors

Marcus C. Chibucos, Deborah A. Siegele, James C. Hu, Michelle Giglio, Chibucos, Marcus C., Siegele, Deborah A., Hu, James C., Giglio, Michelle

Editors

Christophe Dessimoz, Nives Škunca

Abstract

The Evidence and Conclusion Ontology (ECO) is a community resource for describing the various types of evidence that are generated during the course of a scientific study and which are typically used to support assertions made by researchers. ECO describes multiple evidence types, including evidence resulting from experimental (i.e., wet lab) techniques, evidence arising from computational methods, statements made by authors (whether or not supported by evidence), and inferences drawn by researchers curating the literature. In addition to summarizing the evidence that supports a particular assertion, ECO also offers a means to document whether a computer or a human performed the process of making the annotation. Incorporating ECO into an annotation system makes it possible to leverage the structure of the ontology such that associated data can be grouped hierarchically, users can select data associated with particular evidence types, and quality control pipelines can be optimized. Today, over 30 resources, including the Gene Ontology, use the Evidence and Conclusion Ontology to represent both evidence and how annotations are made.

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Mendeley readers

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The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 5%
Spain 1 5%
Unknown 20 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 32%
Researcher 6 27%
Lecturer 1 5%
Professor 1 5%
Student > Doctoral Student 1 5%
Other 2 9%
Unknown 4 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 32%
Biochemistry, Genetics and Molecular Biology 6 27%
Computer Science 2 9%
Mathematics 1 5%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Other 1 5%
Unknown 4 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 January 2022.
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#18,535,896
of 22,957,478 outputs
Outputs from Methods in molecular biology
#7,935
of 13,137 outputs
Outputs of similar age
#311,191
of 421,014 outputs
Outputs of similar age from Methods in molecular biology
#692
of 1,074 outputs
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