Evidence map›Paper›PMID 18096039›Full record

ArticleGenome biology2007

CONTRAST: a discriminative, phylogeny-free approach to multiple informant de novo gene prediction.

Samuel S Gross, Chuong B Do, Marina Sirota, Serafim Batzoglou

Abstract read
In one paragraph

Article in Genome biology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing 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

37 citing papers in PubMed.

  1. Comparative Genome Annotation.Methods in molecular biology (Clifton, N.J.) · 2024
    Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Whole-Genome Alignment and Comparative Annotation.Annual review of animal biosciences · 2019
    Review
  7. Article
  8. Article
  9. Simultaneous gene finding in multiple genomes.Bioinformatics (Oxford, England) · 2016
    Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. A classification of bioinformatics algorithms from the viewpoint of maximizing expected accuracy (MEA).Journal of computational biology : a journal of computational molecular cell biology · 2012
    Review
  18. 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

4 authors.

Samuel S GrossComputer Science Department, Stanford University, Stanford, CA, USA. ssgross@cs.stanford.edu
Chuong B Do
Marina Sirota
Serafim Batzoglou

Funding

POSTDOCTORAL TRAINING IN MEDICAL INFORMATION SCIENCEST15LM007033 · NLM · STANFORD UNIVERSITY · PI SYLVIA KATINA PLEVRITIS · 1985 to 2026
$25.5M
NLM NIH HHS T15 LM007033
6 · The paper itself

Abstract

We describe CONTRAST, a gene predictor which directly incorporates information from multiple alignments rather than employing phylogenetic models. This is accomplished through the use of discriminative machine learning techniques, including a novel training algorithm. We use a two-stage approach, in which a set of binary classifiers designed to recognize coding region boundaries is combined with a global model of gene structure. CONTRAST predicts exact coding region structures for 65% more human genes than the previous state-of-the-art method, misses 46% fewer exons and displays comparable gains in specificity.

Indexed as

GenomicsSoftwareAlgorithmsAnimalsArtificial IntelligenceBase SequenceExonsExpressed Sequence TagsGenome, HumanHumansProteinsSequence AlignmentProteins

Identifiers

PMID18096039
PMCPMC2246271

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.