Towards Retraining-Free Rna Modification Prediction with Incremental Learning

Qiao, Jianbo; Jin, Junru; Wei, Leyi

doi:10.2139/ssrn.4538477

Cited by 1 publication

(1 citation statement)

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“…However, the study of relationships between ncRNAs and diseases faces significant challenges. Firstly, the diversity and complexity of ncRNAs' functions and mechanisms of action present considerable difficulties for systematic research [7,8,9,10,11,12]. Secondly, although databases like MNDR [13], HMDD [14], piRDisease [15] and lnc2Cancer [16] have been developed to summarize and organize RNA-disease relationships, these databases largely rely on manually collected data.…”

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confidence: 99%

RDscan: Extracting RNA-disease relationship from literature Using pre-training model

zhang,

Yang,

Ren

et al. 2024

Preprint

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With the rapid advancements in molecular biology and genomics, a multitude of connections between RNA and diseases has been unveiled, making the efficient and accurate extraction of RNA-disease relationships (RD relationships) from extensive biomedical literature crucial for advancing research in this field. This study introduces RDscan, a novel text mining method developed based on the pre-training and fine-tuning strategy, aimed at automatically extracting RD-related information from a vast corpus of literature using pre-trained biomedical large language models. Initially, we constructed a dedicated RD corpus, comprising 2,082 positive and 2,000 negative statements, alongside an independent test dataset for training and evaluating RDscan. Subsequently, by fine-tuning the Bioformer and BioBERT pre-trained models, RDscan demonstrated exceptional performance in text classification and named entity recognition (NER) tasks. In 5-fold cross-validation, RDscan significantly outperformed traditional machine learning methods. In summary, RDscan represents the first text mining tool specifically designed for RD relationship extraction, and is freely available at https://github.com/ZhangCellab/RDScaning.

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