Evidence map›Paper›PMID 42253319›Full record

ReviewFrontiers in bioinformatics2026

Resources and applications of public biomedical data.

Mingrui Liu, Zelin Ye, Haiyu Liu, Pengzhen Ma, Huaxin Pang, Yaning Li, Qihao Wang, Yikang Shen, Xiaoxia Xie, Yufeng Zhao

Abstract readReview
In one paragraph

Review in Frontiers in bioinformatics, 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

10 authors.

Mingrui Liu *Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Zelin Ye *Department of Infection, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Haiyu LiuData Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Pengzhen MaData Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Huaxin PangData Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Yaning LiData Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Qihao WangData Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Yikang ShenData Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Xiaoxia XieData Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Yufeng ZhaoData Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This review provides a representative overview of public biomedical databases and their use in biomedical research. These resources are categorized into four major types according to their dominant data content: public health databases, clinical databases, comprehensive cohort databases, and omics databases. For each category, we briefly summarize their main characteristics and access pathways. At the application level, we outline their major uses in population health monitoring, clinical research, predictive modeling, and biomarker discovery. At the methodological level, we summarize two complementary research strategies commonly used with these resources, namely, hypothesis-driven and data-driven research. We further discuss the main challenges in using public biomedical databases and emphasize broad principles for rigorous and appropriate use. Overall, public biomedical databases have become an important infrastructure for modern research. This review aims to provide a reference framework for researchers to more efficiently and reliably utilize these resources for scientific exploration and clinical translation.

Indexed as

applicationbig datadata miningguidelinepublic biomedical database

Identifiers

PMID42253319
PMCPMC13236862

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.