Evidence map›Paper›PMID 16893953›Full record

ArticleNucleic acids research2006

The outcomes of pathway database computations depend on pathway ontology.

M L Green, P D Karp

Abstract readEvaluation Study
In one paragraph

Article in Nucleic acids research, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing 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

45 citing papers in PubMed.

  1. Network propagation in bipartite metabolite-reaction graphs for metabolomic data exploration.Metabolomics : Official journal of the Metabolomic Society · 2026
    Article
  2. Article
  3. Article
  4. Proteomic profiling of zinc homeostasis mechanisms inbioRxiv : the preprint server for biology · 2025
    Article
  5. Article
  6. StandardizedFrontiers in pharmacology · 2023
    Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Pathway Analysis: State of the Art.Frontiers in physiology · 2015
    Review
  15. Comparison of human cell signaling pathway databases--evolution, drawbacks and challenges.Database : the journal of biological databases and curation · 2015
    Review
  16. Pathway analysis of the human brain transcriptome in disease.Journal of molecular neuroscience : MN · 2013
    Review
  17. Article
  18. Genetics and epigenetics of rheumatoid arthritis.Nature reviews. Rheumatology · 2013
    Review
  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

2 authors.

M L GreenBioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, CA 94025, USA. green@ai.sri.com
P D Karp

Funding

ENCYCLOPEDIA OF E COLI GENES AND METABOLISMR01RR007861 · NCRR · SRI INTERNATIONAL · PI KARP, PETER D · 1992 to 2005
$4.0M
The MetaCyc Metabolic Pathway DatabaseR33GM070065 · NIGMS · SRI INTERNATIONAL · PI KARP, PETER D · 2004 to 2006
$3.3M
NCRR NIH HHS R01 RR007861NCRR NIH HHS RR07861NIGMS NIH HHS GM70065NIGMS NIH HHS R33 GM070065
6 · The paper itself

Abstract

Different biological notions of pathways are used in different pathway databases. Those pathway ontologies significantly impact pathway computations. Computational users of pathway databases will obtain different results depending on the pathway ontology used by the databases they employ, and different pathway ontologies are preferable for different end uses. We explore differences in pathway ontologies by comparing the BioCyc and KEGG ontologies. The BioCyc ontology defines a pathway as a conserved, atomic module of the metabolic network of a single organism, i.e. often regulated as a unit, whose boundaries are defined at high-connectivity stable metabolites. KEGG pathways are on average 4.2 times larger than BioCyc pathways, and combine multiple biological processes from different organisms to produce a substrate-centered reaction mosaic. We compared KEGG and BioCyc pathways using genome context methods, which determine the functional relatedness of pairs of genes. For each method we employed, a pair of genes randomly selected from a BioCyc pathway is more likely to be related by that method than is a pair of genes randomly selected from a KEGG pathway, supporting the conclusion that the BioCyc pathway conceptualization is closer to a single conserved biological process than is that of KEGG.

Indexed as

Computational BiologyDatabases, GeneticGenomicsMetabolismVocabulary, Controlled

Identifiers

PMID16893953
PMCPMC1540720

What OpenQuestion holds

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Registered trials

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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.