Evidence map›Paper›PMID 21980353›Full record

ArticlePloS one2011

pubmed2ensembl: a resource for mining the biological literature on genes.

Joachim Baran, Martin Gerner, Maximilian Haeussler, Goran Nenadic, Casey M Bergman

Abstract read
In one paragraph

Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed.

  1. Article
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  4. Molecular Mechanisms of Resistance to Ionizing Radiation inAntioxidants (Basel, Switzerland) · 2023
    Article
  5. Article
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  7. Potential alternative drug treatment for bone giant cell tumor.Frontiers in cell and developmental biology · 2023
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
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  14. Article
  15. Article
  16. Text Mining-Based Drug Discovery in Osteoarthritis.Journal of healthcare engineering · 2021
    Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Joachim BaranFaculty of Life Sciences, University of Manchester, Manchester, United Kingdom.
Martin Gerner
Maximilian Haeussler
Goran Nenadic
Casey M Bergman

Funding

Biotechnology and Biological Sciences Research Council BB/G000093/1
6 · The paper itself

Abstract

backgroundThe last two decades have witnessed a dramatic acceleration in the production of genomic sequence information and publication of biomedical articles. Despite the fact that genome sequence data and publications are two of the most heavily relied-upon sources of information for many biologists, very little effort has been made to systematically integrate data from genomic sequences directly with the biological literature. For a limited number of model organisms dedicated teams manually curate publications about genes; however for species with no such dedicated staff many thousands of articles are never mapped to genes or genomic regions. METHODOLOGY/PRINCIPAL

findingsTo overcome the lack of integration between genomic data and biological literature, we have developed pubmed2ensembl (http://www.pubmed2ensembl.org), an extension to the BioMart system that links over 2,000,000 articles in PubMed to nearly 150,000 genes in Ensembl from 50 species. We use several sources of curated (e.g., Entrez Gene) and automatically generated (e.g., gene names extracted through text-mining on MEDLINE records) sources of gene-publication links, allowing users to filter and combine different data sources to suit their individual needs for information extraction and biological discovery. In addition to extending the Ensembl BioMart database to include published information on genes, we also implemented a scripting language for automated BioMart construction and a novel BioMart interface that allows text-based queries to be performed against PubMed and PubMed Central documents in conjunction with constraints on genomic features. Finally, we illustrate the potential of pubmed2ensembl through typical use cases that involve integrated queries across the biomedical literature and genomic data. CONCLUSION/SIGNIFICANCE: By allowing biologists to find the relevant literature on specific genomic regions or sets of functionally related genes more easily, pubmed2ensembl offers a much-needed genome informatics inspired solution to accessing the ever-increasing biomedical literature.

Indexed as

PubMedAnimalsChromosome MappingComputational BiologyDatabase Management SystemsDatabases, GeneticData MiningGenomeGenomicsHumansInformation Storage and RetrievalMEDLINEModels, BiologicalModels, GeneticSoftwareUser-Computer Interface

Identifiers

PMID21980353
PMCPMC3183000

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.