Covariate-moderated Empirical Bayes Matrix Factorization.

William R P Denault, Karl Tayeb, Peter Carbonetto, Matthew Stephens, Jason Willwerscheid

Abstract read

In one paragraph

Article in Advances in neural information processing systems, 2025. 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

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.

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

5 authors.

William R P DenaultDepartments of Statistics and Human Genetics, University of Chicago, Chicago, IL 60637, USA.

Karl TayebDepartments of Statistics and Human Genetics, University of Chicago, Chicago, IL 60637, USA.

Peter CarbonettoDepartments of Statistics and Human Genetics, University of Chicago, Chicago, IL 60637, USA.

Matthew StephensDepartments of Statistics and Human Genetics, University of Chicago, Chicago, IL 60637, USA.

Jason WillwerscheidMathematics and Computer Science, Providence College, Providence, RI 02918, USA.

Funding

Genome analysis: statistical methods and applicationsR01HG002585 · NHGRI · UNIVERSITY OF WASHINGTON · PI MATTHEW STEPHENS · 2002 to 2026

$8.4M

NHGRI NIH HHS R01 HG002585

6 · The paper itself

Abstract

Matrix factorization is a fundamental method in statistics and machine learning for inferring and summarizing structure in multivariate data. Modern data sets often come with "side information" of various forms (images, text, graphs) that can be leveraged to improve estimation of the underlying structure. However, existing methods that leverage side information are limited in the types of data they can incorporate, and they assume specific parametric models. Here, we introduce a novel method for this problem,

Identifiers

PMID42433297

PMCPMC13353771

What OpenQuestion holds

Textmetadata

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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.