Advancing bioinformatics with language models: components, applications, and perspectives
TL;DR
This review provides a comprehensive overview of large language models in bioinformatics, detailing their core components like tokenization and transformer architectures, and their applications across genomics, proteomics, and drug discovery.
Problem / question
While large language models excel in natural language processing, their specific components, architectures, and optimal application strategies for diverse bioinformatics data require synthesis and practical guidance for researchers.
Methods
The authors conducted a comprehensive review of LLM applications in bioinformatics, analyzing tokenization methods for diverse biological data types, transformer model architectures, core attention mechanisms, pre-training processes, and currently available foundation models across genomics, transcriptomics, proteomics, drug discovery, and single-cell analysis.
Key findings
The paper outlines how LLMs are applied across multiple bioinformatics domains, specifically genomics, transcriptomics, proteomics, drug discovery, and single-cell analysis. It details the essential components required for these applications, including specialized tokenization methods for biological data, transformer architectures, and attention mechanisms. Furthermore, it catalogs currently available foundation models and their downstream applications, while providing practical guidance for both users and developers to optimize LLM utilization in biological research.
Why it matters
It provides a foundational roadmap and practical guidance for developers and users to effectively apply and optimize LLMs for complex biological data analysis.
Limitations
The provided text does not specify limitations or caveats of the reviewed models or the study itself.
Takeaway
Large language models have profound potential beyond human language, requiring specialized tokenization and transformer architectures to effectively analyze genomic, transcriptomic, proteomic, and single-cell data.