Evidence map›Paper›PMID 42112113›Full record

ArticleJournal of ginseng research2026

GinDB-AI: An integrated ginsenoside database and AI-driven platform for multidimensional information and biological activity prediction.

Nguyen Doan Hieu Nguyen, Vinoth Kumar Sangaraju, Duong Thanh Tran, Nhat Truong Pham, Jae Youl Cho, Balachandran Manavalan

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of ginseng research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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

5 · Who and what money

Authors and funding

6 authors.

Nguyen Doan Hieu NguyenDepartment of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.
Vinoth Kumar SangarajuDepartment of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.
Duong Thanh TranDepartment of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.
Nhat Truong PhamDepartment of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.
Jae Youl ChoDepartment of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.
Balachandran ManavalanDepartment of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

GinDB is a comprehensive database containing 753 standardized ginsenosides with curated and corrected data from 1963 to 2024. Integrated with GinDB-AI, a deep learning module for predicting bioactivity and physicochemical properties, the platform enables rapid structure-activity analysis and visualization. The freely accessible resource accelerates ginsenoside-based drug discovery and natural product research.

Indexed as

Biological functionsDeep learningGinseng databasePhysicochemical properties

Identifiers

PMID42112113
PMCPMC13149897

What OpenQuestion 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 OpenQuestion graph.