Evidence map›Paper›PMID 18096089›Full record

ReviewGenome biology2007

Gene prediction: compare and CONTRAST.

Paul Flicek

Abstract readReview
In one paragraph

Review in Genome biology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Whole-Genome Alignment and Comparative Annotation.Annual review of animal biosciences · 2019
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. 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

1 author.

Paul FlicekEuropean Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. flicek@ebi.ac.uk

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

CONTRAST, a new gene-prediction algorithm that uses sophisticated machine-learning techniques, has pushed de novo prediction accuracy to new heights, and has significantly closed the gap between de novo and evidence-based methods for human genome annotation.

Indexed as

SoftwareAnimalsArtificial IntelligenceComputational BiologyExpressed Sequence TagsGenomeHumansSequence Alignment

Identifiers

PMID18096089
PMCPMC2246255

What OpenQuestion holds

Textmetadata
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.