Large language models for bioinformatics.

Area of Science:

• Bioinformatics
• Computational Biology
• Artificial Intelligence in Medicine

Background:

• Large language models (LLMs) are rapidly evolving.
• Bioinformatics-specific language models (BioLMs) are emerging as powerful tools.
• A comprehensive analysis of BioLMs is needed.

Purpose of the Study:

• To provide a thorough review of BioLMs.
• To analyze their evolution, classification, and features.
• To examine training methodologies, datasets, and evaluation frameworks.

Main Methods:

• Literature review and analysis of existing BioLMs.
• Classification of BioLMs based on architecture and application.
• Examination of training data, computational methods, and evaluation metrics.

Main Results:

• BioLMs have diverse applications in disease diagnosis, drug discovery, and vaccine development.
• Key challenges include data privacy, interpretability, bias, and domain adaptation.
• Emerging trends point towards more sophisticated biological and clinical applications.

Conclusions:

• BioLMs hold transformative potential in bioinformatics and clinical practice.
• Addressing challenges is crucial for responsible development and deployment.
• Future research should focus on advancing BioLMs for complex biological problems.