Evidence map›Paper›PMID 39372719›Full record

ArticlePeerJ2024

PotatoG-DKB: a potato gene-disease knowledge base mined from biological literature.

Congjiao Xie, Jing Gao, Junjie Chen, Xuyang Zhao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Artificial Intelligence-Assisted Breeding for Plant Disease Resistance.International journal of molecular sciences · 2025
    Review
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

4 authors.

Congjiao XieCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China.
Jing GaoCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China.
Junjie ChenCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China.
Xuyang ZhaoCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Potato is the fourth largest food crop in the world, but potato cultivation faces serious threats from various diseases and pests. Despite significant advancements in research on potato disease resistance, these findings are scattered across numerous publications. For researchers, obtaining relevant knowledge by reading and organizing a large body of literature is a time-consuming and labor-intensive process. Therefore, systematically extracting and organizing the relationships between potato genes and diseases from the literature to establish a potato gene-disease knowledge base is particularly important. Unfortunately, there is currently no such gene-disease knowledge base available. Methods: In this study, we constructed a Potato Gene-Disease Knowledge Base (PotatoG-DKB) using natural language processing techniques and large language models. We used PubMed as the data source and obtained 2,906 article abstracts related to potato biology, extracted entities and relationships between potato genes and related disease, and stored them in a Neo4j database. Using web technology, we also constructed the Potato Gene-Disease Knowledge Portal (PotatoG-DKP), an interactive visualization platform. Results: PotatoG-DKB encompasses 22 entity types (such as genes, diseases, species,

Indexed as

Knowledge BasesPlant DiseasesSolanum tuberosumData MiningDisease ResistanceGenes, PlantNatural Language ProcessingDiseaseKnowledge baseLarge language modelLiterature miningPotato

Identifiers

PMID39372719
PMCPMC11456291

What OpenQuestion holds

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LicenceCC BY
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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.