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Advancing bioinformatics with large language models: components, applications and perspectives.

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Large language models (LLMs) offer powerful AI for bioinformatics, excelling in genomics, transcriptomics, and drug discovery. This review details LLM components and applications, providing guidance for users and developers.

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Area of Science:

  • Bioinformatics
  • Artificial Intelligence
  • Computational Biology

Background:

  • Large language models (LLMs) demonstrate significant capabilities in natural language processing (NLP) due to their deep learning architecture and extensive training data.
  • The application of LLMs in bioinformatics is an emerging area with vast potential, possibly surpassing their performance in human language tasks.
  • Understanding the core components and applications of LLMs is crucial for advancing bioinformatics research.

Purpose of the Study:

  • To provide a comprehensive review of large language models (LLMs) in the field of bioinformatics.
  • To cover essential LLM components, including tokenization, transformer architectures, attention mechanisms, and pre-training.
  • To discuss current foundation models, their applications in genomics, transcriptomics, proteomics, drug discovery, and single-cell analysis, and offer practical guidance.

Main Methods:

  • Review of existing literature on large language models and their applications in bioinformatics.
  • Detailed explanation of key LLM concepts such as tokenization, transformer architecture, and attention mechanisms.
  • Analysis of current foundation models and their downstream applications in various bioinformatics domains.

Main Results:

  • LLMs possess substantial potential for addressing complex challenges in genomics, transcriptomics, proteomics, drug discovery, and single-cell analysis.
  • The review outlines critical aspects of LLMs relevant to bioinformatics, including data tokenization and model architectures.
  • Currently available foundation models and their diverse applications are presented, highlighting the practical utility of LLMs.

Conclusions:

  • Large language models are transformative tools for bioinformatics, offering advanced solutions across multiple biological data types and research areas.
  • Effective utilization of LLMs requires understanding their fundamental mechanisms and adapting them to specific bioinformatics tasks.
  • This review equips researchers and developers with the knowledge to leverage LLMs for innovation and optimization in bioinformatics.