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The Gene Ontology Handbook

Overview of attention for book
Cover of 'The Gene Ontology Handbook'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Primer on Ontologies
  3. Altmetric Badge
    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 16: A Gene Ontology Tutorial in Python
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Citations

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Chapter title
A Gene Ontology Tutorial in Python
Chapter number 16
Book title
The Gene Ontology Handbook
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-3743-1_16
Pubmed ID
Book ISBNs
978-1-4939-3741-7, 978-1-4939-3743-1
Authors

Alex Warwick Vesztrocy, Christophe Dessimoz, Vesztrocy, AW, Dessimoz, C, Vesztrocy, Alex Warwick, Dessimoz, Christophe

Editors

Christophe Dessimoz, Nives Škunca

Abstract

This chapter is a tutorial on using Gene Ontology resources in the Python programming language. This entails querying the Gene Ontology graph, retrieving Gene Ontology annotations, performing gene enrichment analyses, and computing basic semantic similarity between GO terms. An interactive version of the tutorial, including solutions, is available at http://gohandbook.org .

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Mexico 1 3%
Unknown 32 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 21%
Researcher 7 21%
Student > Bachelor 7 21%
Student > Master 3 9%
Lecturer 1 3%
Other 1 3%
Unknown 8 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 26%
Agricultural and Biological Sciences 7 21%
Computer Science 3 9%
Engineering 2 6%
Mathematics 1 3%
Other 2 6%
Unknown 10 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 29 November 2020.
All research outputs
#10,021,633
of 16,309,331 outputs
Outputs from Methods in molecular biology
#2,918
of 9,458 outputs
Outputs of similar age
#160,559
of 295,847 outputs
Outputs of similar age from Methods in molecular biology
#18
of 59 outputs
Altmetric has tracked 16,309,331 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,458 research outputs from this source. They receive a mean Attention Score of 2.7. This one has gotten more attention than average, scoring higher than 65% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 295,847 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.