GeneInsight: Condensing gene set knowledge via language models

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Gene set analysis yields extensive annotations from diverse sources.
• Manual interpretation of these annotations to find biological themes is time-consuming and challenging.
• Existing methods struggle to synthesize information across multiple annotation databases effectively.

Purpose of the Study:

• To develop an automated tool, GeneInsight, for identifying coherent biological themes from gene set analysis.
• To leverage artificial intelligence for streamlining the interpretation of complex functional genomics data.
• To enhance the discovery of biological insights potentially missed by separate examination of annotation sources.

Main Methods:

• Developed GeneInsight, an AI-powered bioinformatics tool.
• Integrated functional annotations from STRING-DB.
• Employed sentence embeddings for semantic clustering of terms.
• Utilized large language model prompting for thematic summarization.

Main Results:

• GeneInsight automates the retrieval and synthesis of functional annotations.
• Semantic clustering effectively groups related biological terms.
• Thematic summaries highlight key biological themes obscured in raw annotation data.
• The tool significantly reduces manual effort in gene set analysis interpretation.

Conclusions:

• GeneInsight offers an efficient, AI-driven solution for interpreting gene set analysis results.
• The tool facilitates the identification of overarching biological themes across multiple annotation sources.
• Automating this process empowers researchers to extract deeper biological insights more rapidly.