Evidence map›Paper›PMID 42677209›Full record

ArticleStatistics & probability letters2027

Generalized linear multiple kernel learning for high-dimensional image data.

Qiyuan Shi, Jian Kang, Yi Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistics & probability letters, 2027. 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

3 authors.

Qiyuan ShiDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.ORCID 0000-0002-6524-9515
Jian KangDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Yi LiDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.

Funding

New Statistical Methods for Modelling Cancer OutcomesR01CA249096 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Yi Li · 2021 to 2026
$2.5M
Detecting racial disparities in cancer survival by integrating multiple high-dimensional observational studiesR01CA269398 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GUHA, SUBHARUP, LI, YI · 2022 to 2025
$1.3M
NCI NIH HHS R01 CA249096NCI NIH HHS R01 CA269398
6 · The paper itself

Abstract

We propose GLIMARK, a sparse generalized linear multiple-kernel framework for high-dimensional structured predictors. It extends additive kernel learning to exponential-family outcomes, supports multi-view and hierarchical feature representations, improves scalability via Nyström approximation, and yields interpretable feature-kernel and representer selection.

Indexed as

Chest X-rayFeature extractionGeneralized linear modelsMultiple kernel learning

Identifiers

PMID42677209
PMCPMC13528703

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.