Evidence map›Paper›PMID 18291023›Full record

ArticleGenome biology2008

Genome wide prediction of HNF4alpha functional binding sites by the use of local and global sequence context.

Alexander E Kel, Monika Niehof, Volker Matys, Rüdiger Zemlin, Jürgen Borlak

Abstract read
In one paragraph

Article in Genome biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. The role of HNF4α in adenocarcinoma.Biochemical Society transactions · 2026
    Review
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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.

Alexander E KelBIOBASE GmbH, Halchtersche Str, 38304 Wolfenbüttel, Germany. ake@biobase.de
Monika Niehof
Volker Matys
Rüdiger Zemlin
Jürgen Borlak

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We report an application of machine learning algorithms that enables prediction of the functional context of transcription factor binding sites in the human genome. We demonstrate that our method allowed de novo identification of hepatic nuclear factor (HNF)4alpha binding sites and significantly improved an overall recognition of faithful HNF4alpha targets. When applied to published findings, an unprecedented high number of false positives were identified. The technique can be applied to any transcription factor.

Indexed as

Artificial IntelligenceGenome, HumanAlgorithmsBase SequenceBinding SitesChromatin ImmunoprecipitationElectrophoretic Mobility Shift AssayHepatocyte Nuclear Factor 4HumansPromoter Regions, GeneticRepetitive Sequences, Nucleic AcidSequence Analysis, DNAHepatocyte Nuclear Factor 4

Identifiers

PMID18291023
PMCPMC2374721

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.