Biomedical generative pre-trained based transformer language model for age-related disease target discovery.

ABSTRACT: Target discovery is crucial for the development of innovative therapeutics and diagnostics. However, current approaches often face limitations in efficiency, specificity, and scalability, necessitating the exploration of novel strategies for identifying and validating disease-relevant targets. Advances in natural language processing have provided new avenues for predicting potential therapeutic targets for various diseases. Here, we present a novel approach for predicting therapeutic targets using a large language model (LLM). We trained a domain-specific BioGPT model on a large corpus of biomedical literature consisting of grant text and developed a pipeline for generating target prediction. Our study demonstrates that pre-training of the LLM model with task-specific texts improves its pe

SUBMITTER: Zagirova D 

PROVIDER: S-EPMC10564439 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature