Evidence map›Paper›PMID 42846695›Full record

ArticleJournal of the American Statistical Association2026

Representation Retrieval Learning for Heterogeneous Data Integration.

Qi Xu, Annie Qu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Statistical Association, 2026. 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

2 authors.

Qi XuDepartment of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA.ORCID 0000-0003-1127-6523
Annie QuDepartment of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, CA.ORCID 0000-0002-8396-7828

Funding

SCH: Individualized learning and prediction for heterogeneous multimodal data from wearable devicesR01CA297869 · NCI · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI Yuqing Guo, Annie Qu · 2024 to 2026
$900k
NCI NIH HHS R01 CA297869
6 · The paper itself

Abstract

In the era of big data, large-scale, multi-source, multi-modality datasets are increasingly ubiquitous, offering unprecedented opportunities for predictive modeling and scientific discovery. However, these datasets often exhibit complex heterogeneity, such as covariates shift, posterior drift, and blockwise missingness, which worsen predictive performance of existing supervised learning algorithms. To address these challenges simultaneously, we propose a novel Representation Retrieval (

Indexed as

Blockwise missing dataExcess risk boundMulti-modality dataMulti-task learningRepresentation learning

Identifiers

PMID42846695
PMCPMC13645369

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.