Advancing bioinformatics with language models: components, applications, and perspectives.
- Open access
- 29 cites
Transformer-based language models have the potential to revolutionize bioinformatics by addressing complex challenges across genomics, transcriptomics, proteomics, and drug discovery.
- Why it matters: Despite their success in natural language processing, the application of large language models in bioinformatics remains underexplored, limiting advances in understanding biological data.
- What they did: The review covers key components such as tokenization, transformer architectures, and pretraining, analyzing existing foundation models and their applications in various bioinformatics fields.
- The result: This work highlights current challenges and offers future design principles, guiding the development of next-generation biological language models to enhance research and practical applications.