Approaches for accelerating microbial gene function discovery using artificial intelligence.

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Microbial genomes contain many genes with unknown functions, hindering comprehensive annotation.
• Traditional methods (experimental and computational homology-based) face scalability and accuracy limitations.

Purpose of the Study:

• To review recent advances in artificial intelligence (AI) for gene function discovery in microbial genomes.
• To discuss future directions for AI-guided genome annotation.

Main Methods:

• Review of computational innovations, particularly deep learning, for gene function prediction.
• Integration of computational and experimental approaches into systematic discovery workflows.

Main Results:

• Deep learning significantly improves prediction accuracy for various protein classes (e.g., transcription factors, enzymes).
• Integrated AI and experimental methods enable faster, more accurate, and comprehensive genome annotation.

Conclusions:

• AI, especially deep learning, is revolutionizing gene function discovery and genome annotation.
• Continued refinement of integrated AI-experimental methods promises deeper understanding of microbial biology.