GDReCo: Fine-grained gene-disease relationship extraction corpus

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

GDReCo: Fine-grained gene-disease relationship extraction corpus

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

GDReCo: Fine-grained gene-disease relationship extraction corpus

Popis výsledku anglicky

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

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computer Methods and Programs in Biomedicine

ISSN

0169-2607

e-ISSN

—

Svazek periodika

266

Číslo periodika v rámci svazku

2025

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

25

Strana od-do

108773

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105002635469