Dataset of miRNA-disease relations extracted from textual data using transformer-based neural networks.

ABSTRACT: MicroRNAs (miRNAs) play important roles in post-transcriptional processes and regulate major cellular functions. The abnormal regulation of expression of miRNAs has been linked to numerous human diseases such as respiratory diseases, cancer, and neurodegenerative diseases. Latest miRNA-disease associations are predominantly found in unstructured biomedical literature. Retrieving these associations manually can be cumbersome and time-consuming due to the continuously expanding number of publications. We propose a deep learning-based text mining approach that extracts normalized miRNA-disease associations from biomedical literature. To train the deep learning models, we build a new training corpus that is extended by distant supervision utilizing multiple external databases. A quantitative e

SUBMITTER: Madan S 

PROVIDER: S-EPMC11300841 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature