Evidence map›Paper›PMID 37197290›Full record

ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2023

Bayesian matrix completion for hypothesis testing.

Bora Jin, David B Dunson, Julia E Rager, David M Reif, Stephanie M Engel, Amy H Herring

Abstract read
In one paragraph

Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2023. 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.

  1. Review
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

6 authors.

Bora JinDuke University, Durham, NC, USA.
David B DunsonDuke University, Durham, NC, USA.
Julia E RagerUniversity of North Carolina at Chapel Hill, Chapel Hill, NC, USA.ORCID https://orcid.org/0000-0002-2882-5042
David M ReifNorth Carolina State University, Raleigh, NC, USA.ORCID https://orcid.org/0000-0001-7815-6767
Stephanie M EngelUniversity of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Amy H HerringDuke University, Durham, NC, USA.

Funding

UNC-CH CENTER FOR ENVIRONMENTAL HEALTH &SUSCEPTIBILITYP30ES010126 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Melissa A. Troester · 2001 to 2026
$36.3M
Scientific Cyberinfrastructure Research ProgramZIAES103385 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI WALKER, VICKIE · 2022 to 2025
$17.4M
Reproducibility and Robustness of Dimensionality ReductionR01ES027498 · NIEHS · DUKE UNIVERSITY · PI HERRING, AMY H · 2017 to 2021
$2.9M
Structured nonparametric methods for mixtures of exposuresR01ES028804 · NIEHS · DUKE UNIVERSITY · PI DUNSON, DAVID BRIAN · 2018 to 2021
$2.2M
NIEHS NIH HHS P30 ES010126NIEHS NIH HHS R01 ES027498NIEHS NIH HHS R01 ES028804
6 · The paper itself

Abstract

We aim to infer bioactivity of each chemical by assay endpoint combination, addressing sparsity of toxicology data. We propose a Bayesian hierarchical framework which borrows information across different chemicals and assay endpoints, facilitates out-of-sample prediction of activity for chemicals not yet assayed, quantifies uncertainty of predicted activity, and adjusts for multiplicity in hypothesis testing. Furthermore, this paper makes a novel attempt in toxicology to simultaneously model heteroscedastic errors and a nonparametric mean function, leading to a broader definition of activity whose need has been suggested by toxicologists. Real application identifies chemicals most likely active for neurodevelopmental disorders and obesity.

Indexed as

Bayesian hierarchical modelbioactivity profileschemical screeningheteroscedasticitylatent factor modelsToxCast/Tox21

Identifiers

PMID37197290
PMCPMC10184491

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.