Evidence map›Paper›PMID 42798420›Full record

ArticleNPJ artificial intelligence2026

Data-efficient biomedical in-context learning: a diversity-enhanced submodular perspective.

Jun Wang, Zaifu Zhan, Qixin Zhang, Mingquan Lin, Meijia Song, Rui Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ artificial intelligence, 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

6 authors.

Jun WangDivision of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN USA.
Zaifu ZhanDivision of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN USA.
Qixin ZhangCollege of Computing and Data Science, Nanyang Technological University, Singapore, Singapore.
Mingquan LinDivision of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN USA.
Meijia SongSchool of Nursing, University of Minnesota, Minneapolis, MN USA.
Rui ZhangDivision of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN USA.

Funding

Racial disparities in access to kidney transplantationR01DK115629 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI R. Adams Dudley, KIRSTEN L. JOHANSEN · 2018 to 2026
$4.5M
Detecting synergistic effects of pharmacological and non-pharmacological interventions for AD/ADRDR01AG078154 · NIA · UNIVERSITY OF MINNESOTA · PI HUA XU, RUI ZHANG · 2022 to 2026
$4.2M
A Translational Informatics Framework to Mine Efficacy and Safety of Dietary SupplementsR01AT009457 · NCCIH · UNIVERSITY OF MINNESOTA · PI RUI ZHANG · 2017 to 2026
$4.1M
COMBINI: connecting COmplementary Medicine evidence and BIological kNowledge to support Integrative HealthU01AT012871 · NCCIH · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI Halil Kilicoglu, Cui Tao · 2024 to 2026
$1.9M
SCH: A New Computational Framework for Learning from Imbalanced Biomedical DataR01CA287413 · NCI · UNIVERSITY OF MINNESOTA · PI CUI, YING, SUN, JU · 2023 to 2025
$1.2M
Mining minority enriched AllofUs data for innovative ethnic specific risk prediction modelingR21MD019134 · NIMHD · UNIVERSITY OF MINNESOTA · PI HOU, JUE, WANG, JINHUA · 2023 to 2024
$436k
NCCIH NIH HHS R01 AT009457NCCIH NIH HHS U01 AT012871NCI NIH HHS R01 CA287413NIA NIH HHS R01 AG078154NIDDK NIH HHS R01 DK115629NIMHD NIH HHS R21 MD019134
6 · The paper itself

Abstract

Recent progress in large language models (LLMs) has leveraged their in-context learning (ICL) abilities to enable quick adaptation to unseen biomedical NLP tasks. By incorporating only a few input-output examples into prompts, LLMs can rapidly perform these new tasks. While the impact of these demonstrations on LLM performance has been extensively studied, most existing approaches prioritize representativeness over diversity when selecting examples from large corpora. To address this gap, we propose

Indexed as

Computational biology and bioinformaticsMathematics and computing

Identifiers

PMID42798420
PMCPMC13612249

What OpenQuestion holds

Textmetadata
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Registered trials

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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.