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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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The gene function prediction challenge: Large language models and knowledge graphs to the rescue.

Rohan Shawn Sunil¹, Shan Chun Lim¹, Manoj Itharajula¹

¹School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore.

Current Opinion in Plant Biology

|November 23, 2024

Summary

Understanding plant gene function remains a major challenge, with only 15% of Arabidopsis thaliana genes experimentally verified. This review explores current gene function prediction methods and proposes leveraging AI, including large language models, to accelerate discovery.

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Area of Science:

Plant Science
Genomics
Bioinformatics

Background:

Elucidating gene function is crucial in plant science, yet experimentally verified functions are limited to ~15% of genes in Arabidopsis thaliana.
Current bioinformatical gene function prediction methods show slow progress in performance and adoption for experimental characterization.

Purpose of the Study:

To review the current status and future trends in gene function elucidation.
To explore recent advancements in gene function prediction approaches.
To discuss the potential of artificial intelligence (AI) in accelerating gene function discovery.

Main Methods:

Literature review of gene function elucidation strategies.
Analysis of current bioinformatical gene function prediction methods.
Exploration of recent AI advancements, including large language models and knowledge graphs.

Main Results:

The field faces challenges in comprehensively verifying gene functions, despite ongoing bioinformatical efforts.
Recent AI developments offer promising avenues for enhancing prediction accuracy and efficiency.
Integrating large language models and knowledge graphs can significantly accelerate the process of gene function prediction.

Conclusions:

Accelerating gene function elucidation is critical for advancing plant science.
AI, particularly large language models and knowledge graphs, presents a transformative opportunity for gene function prediction.
Future research should focus on integrating these AI tools to keep pace with scientific literature and experimental data.