↓ Skip to main content

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

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

1 X user


74 Dimensions

Readers on

29 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
Text Mining to Support Gene Ontology Curation and Vice Versa
Chapter number 6
Book title
The Gene Ontology Handbook
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-3743-1_6
Pubmed ID
Book ISBNs
978-1-4939-3741-7, 978-1-4939-3743-1

Patrick Ruch, Ruch, Patrick


Christophe Dessimoz, Nives Škunca


In this chapter, we explain how text mining can support the curation of molecular biology databases dealing with protein functions. We also show how curated data can play a disruptive role in the developments of text mining methods. We review a decade of efforts to improve the automatic assignment of Gene Ontology (GO) descriptors, the reference ontology for the characterization of genes and gene products. To illustrate the high potential of this approach, we compare the performances of an automatic text categorizer and show a large improvement of +225 % in both precision and recall on benchmarked data. We argue that automatic text categorization functions can ultimately be embedded into a Question-Answering (QA) system to answer questions related to protein functions. Because GO descriptors can be relatively long and specific, traditional QA systems cannot answer such questions. A new type of QA system, so-called Deep QA which uses machine learning methods trained with curated contents, is thus emerging. Finally, future advances of text mining instruments are directly dependent on the availability of high-quality annotated contents at every curation step. Databases workflows must start recording explicitly all the data they curate and ideally also some of the data they do not curate.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 3%
Unknown 28 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 21%
Researcher 4 14%
Student > Doctoral Student 2 7%
Other 2 7%
Student > Bachelor 2 7%
Other 5 17%
Unknown 8 28%
Readers by discipline Count As %
Computer Science 6 21%
Biochemistry, Genetics and Molecular Biology 3 10%
Agricultural and Biological Sciences 3 10%
Medicine and Dentistry 2 7%
Social Sciences 2 7%
Other 2 7%
Unknown 11 38%
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 07 January 2022.
All research outputs
of 22,831,537 outputs
Outputs from Methods in molecular biology
of 13,126 outputs
Outputs of similar age
of 420,186 outputs
Outputs of similar age from Methods in molecular biology
of 1,074 outputs
Altmetric has tracked 22,831,537 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,126 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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 420,186 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,074 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.