GDReCo: Fine-grained gene-disease relationship extraction corpus
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AF4LJWVMB" target="_blank" >RIV/00216208:11320/26:F4LJWVMB - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.cmpb.2025.108773" target="_blank" >http://dx.doi.org/10.1016/j.cmpb.2025.108773</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.cmpb.2025.108773" target="_blank" >10.1016/j.cmpb.2025.108773</a>
Alternative languages
Result language
angličtina
Original language name
GDReCo: Fine-grained gene-disease relationship extraction corpus
Original language description
Background and objective: Understanding gene-disease relationships is crucial for medical research, drug discovery, clinical diagnosis, and other fields. However, there is currently no high-quality, fine-grained corpus available for training Natural Language Processing (NLP) models, which have proven to be effective in knowledge extraction. Methods: This study introduces a novel ontology framework for gene-disease associations, addressing the absence of a formal descriptive system and training corpus for NLP models. Results: We developed the Gene Disease Relationship Extraction Corpus (GDReCo), a refined dataset of over 24,000+ cases, including 2300+ manually annotated and 22,000+ model-predicted instances. BERT-based models trained on this data achieved high F1-scores for ""event"" and ""rel"" relationships, validating its effectiveness for Gene-Disease Relationship Extraction (GDRE) tasks. Conclusions: GDReCo serves as a valuable resource for biomedical research, though ChatGPT's limitations in fine-grained relation extraction are noted. © 2025
Czech name
—
Czech description
—
Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
—
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computer Methods and Programs in Biomedicine
ISSN
0169-2607
e-ISSN
—
Volume of the periodical
266
Issue of the periodical within the volume
2025
Country of publishing house
US - UNITED STATES
Number of pages
25
Pages from-to
108773
UT code for WoS article
—
EID of the result in the Scopus database
2-s2.0-105002635469