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Language models for biological research: a primer.

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  • 1Department of Biomedical Data Science, Stanford University, Stanford, USA.

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This primer introduces biologists to artificial intelligence (AI) language models for biological research. Learn best practices for applying natural language and biological sequence models in your work.

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Area of Science:

  • Computational Biology
  • Artificial Intelligence

Background:

  • Language models are increasingly vital in AI and computational biology.
  • These models process both natural language and biological sequences.

Purpose of the Study:

  • To guide biologists in applying AI language models to biological research.
  • To provide best practices and resources for adapting these technologies.

Main Methods:

  • Review of language model applications in biology.
  • Guidance on adapting natural language processing (NLP) and sequence models.

Main Results:

  • Language models offer powerful tools for biological data analysis.
  • Successful adaptation requires understanding model capabilities and data types.

Conclusions:

  • AI language models are transformative for biological research.
  • Biologists can leverage these tools with proper guidance and resources.