ArticleBMC genomics2025
miCDER: a context-aware transformer model for joint miRNA-disease entity and multi-level regulatory relation extraction.
Article in BMC genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundDysregulation of microRNAs (miRNAs) is closely linked to the progression of diverse human diseases. However, the lack of a standardized, fine-grained dataset of miRNA-disease regulatory interactions and the limited ability of existing methods to capture multi-level regulatory relations hinder full understanding of these mechanisms.
resultsWe constructed a fine-grained, multi-level annotated biomedical dataset covering ten entity types and thirteen relation categories, tailored for modeling microRNA-disease interactions. We then propose miCDER, a transformer-based model that jointly extracts entities and relations through contextual encoding and inter-span transformer attention. miCDER achieves state-of-the-art performance in both named entity recognition (NER F1 = 87.34%) and relation extraction (RE F1 = 77.28%), significantly outperforming all methods, including SpERT, STER, BiLSTM-CRF, and CNN. Compared to the strongest baseline SpERT, our model achieved improvements of 8.84% in NER F1 and 12.35% in RE F1, respectively. To validate generalizability, we evaluated miCDER on two public benchmark datasets and compared it with state-of-the-art methods, achieving competitive performance on CoNLL04 (NER F1 = 88.83%, RE F1 = 72.31%) and superior results on the biomedical ADE dataset (NER F1 = 90.42%, RE F1 = 82.81%). Applied to 27,051 curated miRNA-related text segments from PubMed, miCDER automatically extracted 93,221 high-confidence regulatory triplets to construct the miRNA-disease knowledge graph, MAAD-HCD-KG. Among these, 1,735 targeting relations not found in the microRNA target database (miRTarBase) demonstrate miCDER's ability to capture literature-supported regulatory associations that are not yet included in manually curated databases.
conclusionOur proposed miCDER model can serve as an effective tool for extracting fine-grained regulatory information from biomedical texts. The resultant knowledge graph constitutes a significant resource for the advancement of research on diseases related to microRNA.
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