Automatic extraction of transcriptional regulatory interactions of bacteria from biomedical literature using a BERT-based approach.

ABSTRACT: Transcriptional regulatory networks (TRNs) give a global view of the regulatory mechanisms of bacteria to respond to environmental signals. These networks are published in biological databases as a valuable resource for experimental and bioinformatics researchers. Despite the efforts to publish TRNs of diverse bacteria, many of them still lack one and many of the existing TRNs are incomplete. In addition, the manual extraction of information from biomedical literature ("literature curation") has been the traditional way to extract these networks, despite this being demanding and time-consuming. Recently, language models based on pretrained transformers have been used to extract relevant knowledge from biomedical literature. Moreover, the benefit of fine-tuning a large pretrained model with

SUBMITTER: Varela-Vega A 

PROVIDER: S-EPMC11363960 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature