Evidence map›Paper›PMID 25632107›Full record

ReviewDatabase : the journal of biological databases and curation2015

Comparison of human cell signaling pathway databases--evolution, drawbacks and challenges.

Saikat Chowdhury, Ram Rup Sarkar

Abstract readComparative StudyReview
In one paragraph

Review in Database : the journal of biological databases and curation, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.

0numbers the graph read from it
0cells of the map it votes in
63citing 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

63 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. A workflow for human health hazard evaluation using transcriptomic data and Key Characteristics-based gene sets.Toxicological sciences : an official journal of the Society of Toxicology · 2025
    Article
  6. STCDB4ND: a signal transduction classification database for neurological diseases.Database : the journal of biological databases and curation · 2025
    Article
  7. Review
  8. Review
  9. Pathway-guided architectures for interpretable AI in biological research.Computational and structural biotechnology journal · 2025
    Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article

3 more citing papers are in PubMed but not listed here.

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.

Saikat ChowdhuryChemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhaba Road, Pune, Maharashtra 411008, India and Academy of Scientific & Innovative Research (AcSIR), New Delhi 110 001, India Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhaba Road, Pune, Maharashtra 411008, India and Academy of Scientific & Innovative Research (AcSIR), New Delhi 110 001, India.
Ram Rup SarkarChemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhaba Road, Pune, Maharashtra 411008, India and Academy of Scientific & Innovative Research (AcSIR), New Delhi 110 001, India Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhaba Road, Pune, Maharashtra 411008, India and Academy of Scientific & Innovative Research (AcSIR), New Delhi 110 001, India rr.sarkar@ncl.res.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Elucidating the complexities of cell signaling pathways is of immense importance to gain understanding about various biological phenomenon, such as dynamics of gene/protein expression regulation, cell fate determination, embryogenesis and disease progression. The successful completion of human genome project has also helped experimental and theoretical biologists to analyze various important pathways. To advance this study, during the past two decades, systematic collections of pathway data from experimental studies have been compiled and distributed freely by several databases, which also integrate various computational tools for further analysis. Despite significant advancements, there exist several drawbacks and challenges, such as pathway data heterogeneity, annotation, regular update and automated image reconstructions, which motivated us to perform a thorough review on popular and actively functioning 24 cell signaling databases. Based on two major characteristics, pathway information and technical details, freely accessible data from commercial and academic databases are examined to understand their evolution and enrichment. This review not only helps to identify some novel and useful features, which are not yet included in any of the databases but also highlights their current limitations and subsequently propose the reasonable solutions for future database development, which could be useful to the whole scientific community.

Indexed as

Computational BiologyDatabases, GeneticSignal TransductionHumans

Identifiers

PMID25632107
PMCPMC4309023

What OpenQuestion holds

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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.