Evidence map›Paper›PMID 24359333›Full record

ArticleBMC genetics2013

Bridge: a GUI package for genetic risk prediction.

Chengyin Ye, Qing Lu

Open access · diamondAbstract read
In one paragraph

Article in BMC genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 90% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

2 authors at 1 institution in 2 countries.

Chengyin Ye
Qing LuDepartment of Epidemiology and Biostatistics, Michigan State University, B601 West Fee Hall, 909 Fee Road, 48824 East Lansing, MI, USA. qlu@epi.msu.edu.
Michigan State University · US

Funding

Gene-Gene/Gene-Environment Interactions Associated with Nicotine DependenceK01DA033346 · NIDA · MICHIGAN STATE UNIVERSITY · PI LU, QING · 2013 to 2017
$833k
High-dimensional Statistical Genetic Approach for Family-based Orofacial CleftsR03DE022379 · NIDCR · MICHIGAN STATE UNIVERSITY · PI LU, QING · 2012 to 2013
$441k
NIDA NIH HHS K01 DA033346NIDA NIH HHS K01DA033346NIDCR NIH HHS R03DE022379
6 · The paper itself

Abstract

backgroundRisk prediction models capitalizing on genetic and environmental information hold great promise for individualized disease prediction and prevention. Nevertheless, linking the genetic and environmental risk predictors into a useful risk prediction model remains a great challenge. To facilitate risk prediction analyses, we have developed a graphical user interface package, Bridge.

resultsThe package is built for both designing and analyzing a risk prediction model. In the design stage, it provides an estimated classification accuracy of the model using essential genetic and environmental information gained from public resources and/or previous studies, and determines the sample size required to verify this accuracy. In the analysis stage, it adopts a robust and powerful algorithm to form the risk prediction model.

conclusionsThe package is developed based on the optimality theory of the likelihood ratio and therefore theoretically could form a model with high performance. It can be used to handle a relatively large number of genetic and environmental predictors, with consideration of their possible interactions, and so is particularly useful for studying risk prediction models for common complex diseases.

Indexed as

Models, GeneticSoftwareAlgorithmsArea Under CurveCrohn DiseaseHumansPolymorphism, Single NucleotideRisk FactorsROC CurveUser-Computer Interface

Identifiers

PMID24359333
PMCPMC3878190
OpenAlexW2051590741

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.