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David da Costa Correia1, Francisco M Couto2, Hugo Martiniano3

  • 1Departamento de Promoção da Saúde e Prevenção de Doenças não Transmissiveis, Instituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, Lisboa, 1649-016, Portugal; BioISI - Biosystems and Integrative Sciences Institute, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa, 1749-016, Portugal; Departamento de Informática, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa, 1749-016, Portugal; LASIGE, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa, 1749-016, Portugal.

Journal of Biomedical Informatics
|February 25, 2026
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Summary
This summary is machine-generated.

This study developed a method to extract non-coding RNA (ncRNA) and phenotype relationships from scientific literature using Natural Language Processing (NLP) and Large Language Models (LLMs). The approach achieved a high F1-score, promising for future ncRNA research.

Keywords:
Distant SupervisionLarge Language ModelsNon-coding RNAsRelation ExtractionText mining

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Non-coding RNAs (ncRNAs) play crucial roles in biological processes and disease.
  • Information on ncRNA-phenotype relationships is fragmented across scientific literature.
  • Efficient methods are needed to aggregate and normalize this dispersed data.

Purpose of the Study:

  • To develop a methodology for extracting ncRNA-phenotype relations from scientific articles.
  • To combine Natural Language Processing (NLP) and Large Language Models (LLMs) for this task.
  • To create a high-fidelity dataset and relational corpus for ncRNA research.

Main Methods:

  • Developed an NLP pipeline to aggregate and normalize data from five ncRNA-disease databases.
  • Generated a ncRNA-phenotype relational corpus using Distant Supervision Relation Extraction (DSRE).
  • Applied Large Language Models (LLMs) for Relation Extraction (RE), evaluating performance on a validated corpus subset.

Main Results:

  • Created a high-fidelity ncRNA-phenotype relation dataset with 214,300 relations.
  • Generated a relational corpus (ncoRP) with 35,295 unique relations from 21,608 articles.
  • Achieved a high F1-score of 0.978 using an LLM-based RE methodology.

Conclusions:

  • Successfully created a normalized ncRNA-phenotype dataset and relational corpus.
  • The combined LLM and DSRE methodology demonstrates high performance for automatic relation extraction.
  • The developed dataset, corpus, and methodology are valuable resources for ncRNA studies and can be applied to similar biological relation extraction tasks.