Evidence map›Paper›PMID 21346954›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2010

An efficient bayesian method for predicting clinical outcomes from genome-wide data.

Gregory F Cooper, Pablo Hennings-Yeomans, Shyam Visweswaran, Michael Barmada

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Novel Application of Junction Trees to the Interpretation of Epigenetic Differences among Lung Cancer Subtypes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2015
    Article
  7. Article
  8. Article
  9. Survival prediction and treatment recommendation with Bayesian techniques in lung cancer.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2012
    Article
  10. Article
  11. Article
  12. 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.

Gregory F CooperDepartment of Biomedical Informatics.
Pablo Hennings-Yeomans
Shyam Visweswaran
Michael Barmada

Funding

Predicting Patient Outcomes from Clinical and Genome-Wide DataR01LM010020 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI COOPER, GREGORY F. · 2009 to 2010
$1.2M
NLM NIH HHS R01 LM010020
6 · The paper itself

Abstract

This paper compares the predictive performance and efficiency of several machine-learning methods when applied to a genome-wide dataset on Alzheimer's disease that contains 312,318 SNP measurements on 1411 cases. In particular, a Bayesian algorithm is introduced and compared to several standard machine-learning methods. The results show that the Bayesian algorithm predicts outcomes comparably to the standard methods, and it requires less total training time. These results support the further development and evaluation of the Bayesian algorithm.

Indexed as

AlgorithmsBayes TheoremGenomeGenome-Wide Association StudyHumansPolymorphism, Single NucleotidePrognosis

Identifiers

PMID21346954
PMCPMC3041321

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.