A novel targeted learning method for quantitative trait loci mapping.

Hui Wang, Zhongyang Zhang, Sherri Rose, Mark van der Laan

Abstract read

In one paragraph

Article in Genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Robust Machine Learning Variable Importance Analyses of Medical Conditions for Health Care Spending.Health services research · 2018
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.

Hui WangPalo Alto Veterans Institute for Research, Palo Alto, California 94304 Hui.Wang@va.gov.

Zhongyang ZhangDepartment of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029.

Sherri RoseDepartment of Health Care Policy, Harvard Medical School, Cambridge, Massachusetts 02115.

Mark van der LaanDivision of Biostatistics, University of California, Berkeley School of Public Health, Berkeley, California 94720.

Funding

Targeted Learning using adaptive designs for HIV Epidemic control in East AfricaR01AI074345 · NIAID · UNIVERSITY OF CALIFORNIA BERKELEY · PI PETERSEN, MAYA LIV, VANDERLAAN, MARK J · 2007 to 2023

$6.6M

NIAID NIH HHS R01 AI074345

6 · The paper itself

Abstract

We present a novel semiparametric method for quantitative trait loci (QTL) mapping in experimental crosses. Conventional genetic mapping methods typically assume parametric models with Gaussian errors and obtain parameter estimates through maximum-likelihood estimation. In contrast with univariate regression and interval-mapping methods, our model requires fewer assumptions and also accommodates various machine-learning algorithms. Estimation is performed with targeted maximum-likelihood learning methods. We demonstrate our semiparametric targeted learning approach in a simulation study and a well-studied barley data set.

Indexed as

Models, GeneticModels, StatisticalQuantitative Trait LociAlgorithmsChromosome MappingComputer SimulationCrosses, GeneticDatasets as TopicGenetic MarkersHordeumQuantitative Trait, HeritableGenetic Markersexperimental crossesQTL mappingsemiparametric modeltargeted maximum-likelihood estimation

Identifiers

PMID25258376

DOI10.1534/genetics.114.168955

PMCPMC4256757

What Socratic 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 Socratic graph.