Evidence map›Paper›PMID 18495754›Full record

ArticleNucleic acids research2008

Discovery of novel tumor suppressor p53 response elements using information theory.

Ilya G Lyakhov, Annangarachari Krishnamachari, Thomas D Schneider

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.8field-weighted citation impact, top 9% of its field
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

16 citing papers in PubMed, 32 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. p53 Protects lung cancer cells against metabolic stress.International journal of oncology · 2010
    Article
  10. Article
  11. A brief review of molecular information theory.Nano communication networks · 2010
    Article
  12. Mechanisms of transcription factor selectivity.Trends in genetics : TIG · 2010
    Review
  13. Article
  14. p53 and metabolism.Nature reviews. Cancer · 2009
    Review
  15. Article
  16. Review
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

3 authors at 1 institution in 1 country.

Ilya G LyakhovBasic Research Program, SAIC-Frederick, Inc., NCI at Frederick, Frederick, MD, USA.
Annangarachari Krishnamachari
Thomas D Schneider
Jawaharlal Nehru University · IN

Funding

Molecular Information TheoryZIABC008396 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI SCHNEIDER, THOMAS · 2009 to 2010
$906k
NCI NIH HHS N01 CO012400NCI NIH HHS N01-CO-12400
6 · The paper itself

Abstract

An accurate method for locating genes under tumor suppressor p53 control that is based on a well-established mathematical theory and built using naturally occurring, experimentally proven p53 sites is essential in understanding the complete p53 network. We used a molecular information theory approach to create a flexible model for p53 binding. By searching around transcription start sites in human chromosomes 1 and 2, we predicted 16 novel p53 binding sites and experimentally demonstrated that 15 of the 16 (94%) sites were bound by p53. Some were also bound by the related proteins p63 and p73. Thirteen of the adjacent genes were controlled by at least one of the proteins. Eleven of the 16 sites (69%) had not been identified previously. This molecular information theory approach can be extended to any genetic system to predict new sites for DNA-binding proteins.

Indexed as

Information TheoryResponse ElementsBinding SitesGene Expression RegulationHumansModels, GeneticTranscription Initiation SiteTumor Suppressor Protein p53Tumor Suppressor Protein p53

Identifiers

PMID18495754
PMCPMC2441790
OpenAlexW2032214039

What OpenQuestion holds

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LicenceCC BY-NC
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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.