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A Survey of Biological Function Prediction Methods with Focus on Natural Language Processing (NLP) and Large Language

Dana Mary Varghese1, T Athulya1, Vikash K Mohani1

  • 1School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.

Methods in Molecular Biology (Clifton, N.J.)
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Summary
This summary is machine-generated.

This survey reviews protein function prediction models, focusing on natural language processing (NLP) and large language models (LLMs). It highlights advances in using sequence, structure, and literature for automated function annotation.

Keywords:
Function predictionGene ontologiesLarge language modelsNatural language processingProtein function

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Understanding protein function is crucial for deciphering biological processes.
  • Existing methods for protein function prediction rely on sequence, structure, gene expression, and literature mining.
  • Automating function prediction is essential for large-scale biological data analysis.

Purpose of the Study:

  • To survey current models for protein function prediction.
  • To focus on advances in natural language processing (NLP) and large language models (LLMs) for this task.
  • To identify remaining challenges in automating function prediction from protein sequences and literature.

Main Methods:

  • Review of existing literature on protein function prediction models.
  • Analysis of NLP techniques applied to biological text for function annotation.
  • Evaluation of large language models (LLMs) for encoding protein sequence and structure data.
  • Comparison of different approaches for function prediction.

Main Results:

  • NLP and LLM-based models offer powerful new avenues for rapid protein function annotation.
  • Significant progress has been made in leveraging these advanced AI techniques.
  • Key challenges remain in fully automating function prediction from diverse data sources.

Conclusions:

  • NLP and LLMs are transforming protein function prediction.
  • Further research is needed to overcome limitations and achieve complete automation.
  • These models hold great promise for advancing biological understanding.